# AERIQ: SDR-Based LTE I/Q Measurement and Analysis Framework for Air-to-
Ground Propagation Modeling

S. J. Maeng, İ. Güvenç, M. L. Sichitiu, R. Dutta, O. Ozdemir, and M. Mushi
Department of Electrical and Computer Engineering, North Carolina State
University, Raleigh, NC
<EMAIL_ADDRESS>
###### Abstract

Radio dynamic zones (RDZs) are areas or volumes with automatic spectrum
management mechanisms that control electromagnetic energy entering, escaping,
or occupying the zone. In order to have real-time volumetric spectrum
awareness in an RDZ, it is critical to understand the propagation
characteristics throughout that RDZ, such as time, frequency, and spatial
correlation for various communications scenarios and configurations. This
requires wireless measurement campaigns that can be carried out flexibly and
are repeatable for different experiment configurations, parameters, and
transmitter/receiver locations. Such measurements should also provide raw data
from which various key parameters of interest (KPIs) can be conveniently
extracted for further analysis. In this paper, we propose AERIQ: a software-
defined radio (SDR) based I/Q measurement and analysis framework that is
flexible, repeatable, and provides raw I/Q samples for post-processing the
data to extract various KPIs. Using SDRs, we collect I/Q data with unmanned
aerial vehicles (UAVs) flying at various different altitudes in an RDZ-like
outdoor environment, from a 4G LTE eNB that we configure to operate at 3.51
GHz. Using the raw I/Q samples, and using Matlab’s LTE Toolbox, we provide a
step-by-step description for the following post-processing stages of an aerial
receiver: frequency offset estimation/correction, synchronization, cell
search, channel estimation, and reference signal received power (RSRP). We
provide various representative results for each step, such as RSRP
measurements and corresponding analytical approximation at different UAV
altitudes, and coherence bandwidth of the channel at different UAV altitudes
and link distances. The collected raw data as well as the software developed
for obtaining and post-processing such data are provided publicly for
potential use by other researchers.

##  I Introduction

As the demand for advanced wireless communications rapidly rises, such as
supporting high transmission speed, low latency, and massive connectivity, it
is important to secure available spectrum resources. In this sense, efficient
and flexible spectrum management systems that can help utilize unoccupied and
under-utilized spectrum resources are needed. A radio dynamic zone (RDZ) is an
area or a volume with automatic spectrum management mechanisms that control
electromagnetic energy entering, escaping, or occupying the zone. RDZs can
help develop, test, and improve spectrum technologies in the future. In an
RDZ, spectrum should be monitored and controlled in real-time to share the
spectrum resources with incumbent users outside of the zone [1, 2].

Spectrum sensing and monitoring across the whole RDZ volume is challenging and
complicated, as it is not feasible to deploy sensor nodes at every location
across a large RDZ area. However, spectrum measurements obtained at sparsely
located sensor nodes can be a reasonable approach for monitoring spectrum
occupancy across an RDZ. For example, in [3], radio environment maps (REMs)
have been used for monitoring the coverage of cellular networks. In [4, 5],
Kriging has been studied to spatially interpolate the signal power by using
spectrum measurements from nearby locations. In [6, 7, 8], cooperative and
crowd-sourced spectrum sensing have been investigated. In [9], a signal
leakage power monitoring technique is proposed for RDZs using sparsely
deployed ground sensor nodes.

Figure 1: Block diagram for post-processing of LTE I/Q samples collected at an
SDR that is carried by a UAV.

In order to have real-time volumetric spectrum awareness in an RDZ, it is
critical to understand the propagation characteristics throughout that RDZ,
such as time, frequency, and spatial correlation for various communications
scenarios, transmitter/receiver configurations, and environmental conditions.
In particular, an accurate understanding of the characteristics of air-to-
ground channel propagation is critical to generate a real-time and volumetric
radio map for spectrum monitoring. In the literature, [10] measures and models
air-to-ground path loss at an open rural area with 5 different heights from 20
m to 100 m using a cellular smartphone-mounted UAV, and Qualipoc software is
used to obtain reference signal received power (RSRP). In [11], The LTE uplink
cellular network is tested and analyzed by using a UAV carrying a smartphone
with QualiPoc software from Rohde & Schwarz at two heights, 1.5 m, and 100 m.
In [12], two different heights of smartphone-mounted UAVs are used to collect
measurements from public LTE networks. Three different measurement software,
namely, Keysight NEMO Outdoor, Rohde & Schwarz QualiPoc, and InfoVista TEMS
are utilized to obtain four different key parameters of interest (KPIs):
physical resource blocks (PRBs) utilization, modulation and coding scheme
(MCS) level, throughput, and transmit power. Data collected through such
commercial smartphone software is limited to the set of KPIs that are
supported by those software and it is not possible to extract any additional
information. Collecting raw I/Q samples in the desired band and frequency of
interest would allow flexibility to extract any KPIs and test new approaches
for receiver processing.

In this paper, we propose AERIQ: a software-defined radio (SDR) based I/Q
measurement and analysis framework that is flexible, repeatable, and provides
raw I/Q samples for post-processing the data to extract various KPIs. We
conduct experiments at the NSF Aerial Experimentation and Research Platform
for Advanced Wireless (AERPAW) Lake Wheeler Field Labs site [13] in an open
rural area. Using SDRs, we collect I/Q data with unmanned aerial vehicles
(UAVs) flying at various different altitudes in an RDZ-like outdoor
environment, from a 4G LTE eNB that we configure to operate at 3.51 GHz. Using
the raw I/Q samples, and using Matlab’s LTE Toolbox, we provide a step-by-step
description for the following post-processing stages of an aerial receiver:
frequency offset estimation/correction, synchronization, cell search, channel
estimation, and RSRP calculation (see Fig. 1). We provide various
representative results for each step, such as RSRP measurements and
corresponding analytical approximation at different UAV altitudes and the
coherence bandwidth of the channel at different UAV altitudes and link
distances.

The rest of this paper is organized as follows. In Section II, we describe a
measurement environment and setup and elaborate on a UAV architecture and
trajectory design. In Section III, we evaluate estimated carrier frequency
offset (CFO) in different RSRP levels. In Section IV, timing offset (TO)
detection and the cell search process are carried out using LTE
synchronization sequences. In Section V, we show the estimated frequency
channel response obtained by cell-specific reference signals (CRSs) in
different heights and RSRPs. In Section VI, we analyze and fit RSRPs with
different heights by two-ray path loss model and antenna radiation patterns.
In Section VII, the coherence bandwidth with different heights is shown, which
is obtained by the channel estimation results. In Section VIII, we analyze and
fit shadowing components and spatial correlation. In Section IX, we summarize
the air-to-ground channel measurements and analysis results.

##  II Measurement Campaign

In the following subsection, we first briefly review the concept of RDZs and
describe conducted experiments to measure and analyze the channel propagation
between the transmitter on the ground and the aerial sensor node. Fig. 1
describes how I/Q samples from measurements are proceeded by a block diagram
in AERIQ. First, the CFO is estimated by a cyclic prefix (CP). Then, the
timing offset and the physical cell identities (PCIs) are jointly estimated by
the correlation of a primary synchronization signal (PSS) and secondary
synchronization signal (SSS). After PCI is obtained, CRSs can be extracted and
they are used in the channel estimation and the RSRP calculation.

Figure 2: The illustration of an RDZ with aerial and ground sensors and users.
The experiments carried out within an RDZ should not interfere with the users
outside of the RDZ.

###  II-A Measurement Environment

Before we describe our measurement setup, we will first overview our
measurement environment. In this work, we consider an RDZ-like outdoor
environment for advanced wireless and UAV experimentation as shown in Fig. 2
and Fig. 3. RDZs are the geographical areas where the spectrum is monitored
continuously to facilitate spectrum sharing with other transmissions within
and outside of the RDZ. To achieve that, spectrum sensing results across the
RDZ and in the frequencies of interest are aggregated at a central processing
unit, to determine whether the signal leakage from the zone results in
interference problems with licensed receivers outside and potentially inside
the RDZ. For instance, if the signal leakage power level at the boundary of an
RDZ exceeds a predefined threshold level, a transmission inside the zone is
automatically terminated. For continuous spectrum monitoring in the 3D space,
ground and aerial spectrum sensor nodes are deployed in RDZ. In this paper, we
consider measurements at one such monitoring node that is carried at a UAV at
different altitudes.

###  II-B Measurement Setup

Figure 3: AERPAW’s Lake Wheeler Field Labs site where air-to-ground I/Q
samples have been collected.

The measurement campaign is executed at the NSF AERPAW Lake Wheeler Road Field
Labs site in Raleigh NC, USA, which is shown in Fig. 3. A fixed BS tower with
a single dipole transmit antenna is deployed, and the flying UAV is equipped
with a vertically oriented single dipole receiver antenna and a GPS receiver.
LTE eNB is realized by the srsRAN open-source SDR software at the BS tower
that transmits the LTE downlink signal. The SDR is mounted on the UAV to
collect raw I/Q data samples. The UAV collects 20 ms segments of data out of
every 100 ms, and out of that recording, and extracts a full LTE frame that is
of length 10 ms. In other words, in every 100 ms, we capture one full LTE
frame. The SDR of USRP B205mini from NI is adopted at both the tower and the
UAV. The detailed specifications of the BS tower and the UAV receiver are
listed in Table I.

###  II-C UAV Architecture and Trajectory

Figure 4: Illustration of the AERPAW UAV that carries a portable node
including an SDR [14]. Figure 5: Trajectory and RSRP at UAV altitude of 70 m.

In the experiments, we use a custom built UAV that carries a portable node as
shown in Fig. 4 [14]. The portable node includes an NI USRP B205mini SDR that
executes a Python script [15] to collect I/Q samples at the desired center
frequency with the desired sampling rate. The UAV that carries the portable
node flies on a predesigned trajectory at a fixed height. In particular, the
UAV moves on a _zig-zag_ path through the experiment site and flies back to
the starting position. The flights are conducted multiple times by increasing
the height of the UAV (30 m, 50 m, 70 m, 90 m, 110 m), following an identical
horizontal trajectory at each altitude. As an example, the UAV trajectory at
an altitude of 70 m as well as the RSRP observed at each UAV location is shown
in Fig. 5.

TABLE I: Measurement Setup for Experiments BS Tower (Transmitter) |
---|---
Technology | LTE
Tower height | 10 m
Transmit power | 10 dBm
Carrier frequency | 3.51 GHz
Bandwidth | 1.4 MHz
Antenna | Dipole antenna (RM-WB1)
UAV (Receiver) |
Antenna | Dipole antenna (SA-1400-5900)
UAV heights | {30, 50, 70, 90, 110} m

###  II-D Altitude and Speed of the UAV by GPS Reading

Figure 6: Altitude of the UAV from GPS logs versus time for the five different
experiments. Figure 7: UAV speed versus time from GPS logs at UAV altitude 70
m.

During the flight, the UAV flies up to the predefined altitude and flies
horizontally at that altitude until landing. Fig. 6 shows the change of the
UAV altitude versus time for experiments at different altitudes, which is
obtained from the log of the GPS mounted on the UAV. Fig. 7 shows the UAV
speed during the flight. The UAV speed can be obtained by the GPS locations
and timestamps. It is shown that the UAV flies with constant speed and the UAV
quickly slows down the speed when it changes direction at a certain waypoint,
and it quickly recovers the speed right after that.

The GPS logs give information on the timestamp, the latitude, the longitude,
and the altitude during flights, which is used to generate the UAV trajectory
in Fig. 5 as well.

After we collect raw I/Q samples from the experiments, we process data to
analyze the characteristic of the channel propagation by using MATLAB LTE
Toolbox [16, 17]. The post-processing includes cell ID search,
synchronization, and channel estimation as well as obtaining the RSRP.

##  III Carrier Frequency Offset Estimation

(a) 70 m height at 380 s (70 m, RSRP high). (b) 70 m height at 520 s (70 m,
RSRP low).

Figure 8: Moving correlation with CP length window for 1 slot length (7 OFDM
symbols, 0.5 ms). The peak position indicates the starting of the CP. Figure
9: Estimated CFO (fracton of subcarrier spacing) versus time at UAV altitude
of 70 m.

The CFO can occur from the instability of the oscillators between Tx and Rx,
or Doppler shift(s) that occur due to the velocity difference between a
transmitter and a receiver. In an LTE system, the CFO can be estimated in the
time-domain waveform by using the CP, which is the repeated version of the
fraction of the orthogonal frequency-division multiplexing (OFDM) symbols
attached in front of the OFDM symbols. The time-domain OFDM signal of the CP
with CFO can be represented as follows:

$\displaystyle y[n]$ $\displaystyle=x[n]e^{j2\pi n\delta/N},$ (1)

where $\delta$, $N$ denotes CFO and the number of subcarriers. Note that the
unit for the CFO $\delta$ is subcarrier spacing. The repeated OFDM signal
corresponding to the CP is given by

$\displaystyle y[n+N]$
$\displaystyle=x[n+N]e^{j2\pi(n+N)\delta/N}=x[n]e^{j2\pi\delta+j2\pi
n\delta/N}.$ (2)

The CFO can be obtained by the correlation between CP and the corresponding
OFDM symbol, as

$\displaystyle\hat{\delta}$
$\displaystyle=\frac{1}{2\pi}\arg\left(\frac{1}{{\rm L}_{\rm
cp}}\sum_{n=0}^{{\rm L}_{\rm cp}-1}x[n]^{\star}x[n+N]\right),$ (3)

where ${\rm L}_{\rm cp}$ is the length of the CP and $(\cdot)^{\star}$
indicates the complex conjugate operation.

We use collected LTE I/Q samples from experiments to evaluate the reliability
of the CFO estimation. We consider the I/Q measurements at the UAV altitude of
70 m for evaluation. Fig. 8 shows the $N$-shifted moving auto-correlation of
the time-domain received signal with the ${\rm L}_{\rm cp}$ window size for 1
slot length to estimate the CFO $\delta$. The peak position indicates the
starting of the CP. We show the correlations at two different UAV locations,
where the RSRP is high and low, as marked in Fig. 13. It is observed that the
correlation peaks for each OFDM symbol are clearly visible at high RSRP, while
they are not as easy to distinguish at low RSRP.

Fig. 9 shows the estimated CFO versus time using (3) at the UAV altitude of 70
m in Fig. 5 and Fig. 6. It is observed that the estimated CFO ($\hat{\delta}$)
fluctuates between -0.13 and -0.09 during a whole flight, which shows that the
trend of the estimated CFO is matched with the contant UAV speed in Fig. 7,
while high sparks are also observed many times, which results from the
estimation error at low RSRP. The estimated CFO is compensated before the
timing synchronization and cell search stage which is dicusssed in next
section.

##  IV Timing Offset Detection and Cell Search

(a) Correlation by PSS (RSRP high, 70 m, 380 s). (b) Correlation with both
PSS, SSS subframe 0, 5 (RSRP high, 70 m, 380 s). (c) Correlation by PSS (RSRP
low, 70 m, 520 s). (d) Correlation with both PSS and SSS subframe 0, 5 (RSRP
low, 70 m, 520 s).

Figure 10: The correlation of synchronization sequences to detect the timing
offset at high RSRP ((a) and (b)) and low RSRP ((c) and (d)) locations.

The time offset from the received signal can be detected by using LTE
synchronization sequences: PSS and SSS. The PSS is generated from Zadoff–Chu
sequences and the SSS is designed using M-sequences, both of which have good
auto-correlation properties [18]. The correlation peak location indicates the
position of the received PSS and SSS, both of which are always carried at the
first slot of subframes 0 and 5 within a frame for FDD-based LTE transmission.
Timing offset can be found by the correlation of PSS as well as the
combination of the correlation of the PSS, the SSS subframe 0, and subframe 5
to improve the accuracy of the detection. More specifically, individual
correlations of the incoming received signal with the PSS and the SSS subframe
0, and subframe 5 can be added to improve the detection performance. The
correlation of the SSS subframe 5 is cyclic-shifted to align with the SSS
subframe 0 correlation when three different correlations are summed. The
timing offset detection from synchronization signals can be expressed as

$\displaystyle\hat{m}$ $\displaystyle=\arg\max_{m}\left(\frac{1}{{\rm L}_{\rm
ss}}\sum_{n=0}^{{\rm L}_{\rm ss}-1}y[n+m]s[n]^{\star}\right),$ (4)

where $s[n]$ and ${\rm L}_{\rm ss}$ denote the synchronization signal and the
length of the synchronization signal, respectively. Note that while we
consider non-coherent detection for both the PSS and the SSS in this work, the
SSS can be detected by coherent detection as well as discussed in [18, Ch. 7].

The PSS and SSS distinguish 504 unique PCIs, which can be estimated by the
combination of two different ID groups from PSS and SSS. The physical layer ID
0 - 2 is given by the PSS, and another physical layer group ID 0 - 167 is
provided by the SSS. Among all possible physical layer ID cases, the physical
layer ID that achieves the highest correlation peak of the synchronization
signals is chosen as the correct physical layer ID. The PCI (${\rm N}_{\rm
pci}$) is estimated by

$\displaystyle\hat{{\rm N}}_{\rm pci}$ $\displaystyle=\hat{{\rm N}}_{\rm
ID}^{\rm pss}+3\hat{{\rm N}}_{\rm ID}^{\rm sss},$ (5)

where $\hat{{\rm N}}_{\rm ID}^{\rm pss}$, $\hat{{\rm N}}_{\rm ID}^{\rm sss}$
indicate the estimated physical layer ID and group ID, from the PSS and the
SSS, respectively.

Fig. 10 shows the correlation of the synchronization sequences to detect the
timing offset. We observe correlations at high and low RSRP at 70 m height
measurement. We can observe that the peak is relatively high and clear when we
find the timing offset combing PSS and SSSs. Furthermore, it is observed that
the peaks are detected at low RSRP as well, which implies that the timing
offset is properly detected at low RSRP.

##  V Channel Estimation

(a) 70 m height at 380 s (RSRP high) (b) 70 m height at 520 s (RSRP low)

Figure 11: Received signal power in LTE resource grid of duration six
subframes and having a bandwidth of 1.08 MHz.

(a) 70 m height at 380 s (RSRP high)

(b) 110 m height at 380 s (RSRP high)

(c) 70 m height at 520 s (RSRP low)

(d) 110 m height at 520 s (RSRP low)

Figure 12: The estimated channel of 70 m and 110 m height flights at the time
that RSRP is low and high.

After the synchronization and the cell search procedures end, the LTE CRS,
which is sparsely allocated in the resource grid, can be extracted in the
frequency domain to estimate the channel. The CRS transmitted by a certain eNB
is uniquely connected to the PCI of that eNB, calculated in (5). In
particular, the CRS location is shifted by $\hat{{\rm N}}_{\rm
pci}~{}\textrm{mod}~{}6$ in the frequency domain. Moreover, the CRS for a
given cell is generated from a Gold sequence that is uniquely characterized by
the PCI of that cell.

The channel coefficients of the CRS locations can be estimated by the least
square (LS) method and then channel coefficients of the unknown locations can
be interpolated by cubic interpolation [18, Ch. 8]. Fig. 11 shows the received
signal power in the frequency domain over the LTE resource grid that has a
bandwidth of $1.08$ MHz and is observed over $6$ subframes. It can be seen
that the physical broadcast channel (PBCH) is transmitted at subframe 0, the
synchronization signals are allocated at subframe 0 and 5, and the CRSs are
continuously transmitted across the whole grid with a specific pattern. Note
that we set up the experiments so that other LTE data and control channels are
not transmitted during flights, which can be observed by the unoccupied
resources in the received signal in Fig. 11.

The estimated time and frequency domain channel for UAV altitudes of 70 m and
110 m are shown in Fig. 12. After the UAV reaches the desired height and the
UAV is located close to the BS tower, the RSRP is high and the channel in the
frequency domain varies only slightly for both altitudes of 70 m and 90 m as
shown in Fig. 12a and Fig. 12b, respectively. However, when the UAV is far
away from the BS tower in Fig. 12c and Fig. 12d, RSRP is low and the channel
in both time and frequency domains fluctuate, which is more visible for the 70
m UAV height when compared with the 90 m UAV height.

##  VI RSRP Calculation from CRS

Figure 13: Change of the RSRP over time at different altitudes.

(a) Tx antenna (2.6 GHz) (b) Rx antenna (2.4 GHz)

Figure 14: The dipole antenna radiation patterns.

(a) 30 m height (b) 70 m height (c) 90 m height

Figure 15: The RSRP fitting using free-space and two-ray path loss models.

RSRP indicates the average signal strength that is calculated over the CRS and
it averages out the effects of interference. Fig. 13 shows the RSRP obtained
during the experiments at different UAV altitudes. It is observed that the
RSRP changing tendency is similar at all heights, which results from using
identical horizontal trajectories for different flights. Because of the
pattern of the UAV trajectory, the RSRP increases and decreases multiple times
during the flights, based on the distance between the Tx tower and the UAV. We
consider two extremes, 380 seconds and 520 seconds as marked on Fig. 13, to
correspond to high-RSRP and low-RSRP locations, respectively. Representative
results on CFO estimation, timing offset estimation, received power over the
resource grid, and channel estimation have been provided at these two
locations earlier in this paper.

We also analyze the measured RSRP by fitting it to different path loss models.
In particular, we consider the free-space path loss model and the two-ray path
loss model with 3D antenna radiation pattern for the Tx and the Rx. The
antenna patterns at the elevation angle domain, which are provided by the
antennas specification sheets [19, 20], are shown in Fig. 14. Since the
antenna specification sheets do not provide the data at 3.5 GHz carrier
frequency that we carried the experiments at, we used radiation patterns from
other carrier frequencies available in the specifications. Since we use dipole
antennas for both the Tx and the Rx, the omni-directional antenna radiation
can be assumed at the azimuth plane. The two-ray path loss model considers the
line-of-sight and the strong ground reflected paths which are combined at the
received signal. The two-ray path loss model is given as

$\displaystyle\mathsf{PL}=\left(\frac{\lambda}{4\pi}\right)^{2}$
$\displaystyle\times\left|\frac{\sqrt{\mathsf{G}_{\rm
bs}(\theta_{l})\mathsf{G}_{\rm uav}(\theta_{l})}}{d_{\rm
3D}}+\frac{\Gamma(\theta_{r})\sqrt{\mathsf{G}_{\rm
bs}(\theta_{r})\mathsf{G}_{\rm
uav}(\theta_{r})}e^{-j\Delta\tau}}{r_{1}+r_{2}}\right|^{2},$ (6)

where $\mathsf{G}_{\rm bs}(\theta)$, $\mathsf{G}_{\rm uav}(\theta)$, $\lambda$
denote the Tx antenna gain, the Rx antenna gain, and the wave-length,
respectively, $\theta_{r}$ represents ground reflection angle, $\Delta\tau$
indicates the phase difference between two paths, and $\Gamma(\theta_{r})$
denotes the ground reflection coefficient that is dependent on the material on
the ground. In addition, $d_{\rm 3D}$, $r_{1}$, $r_{2}$ respectively represent
the LoS distance from Tx to Rx, the distance from Tx to the ground of the
ground reflection path, and the distance from the ground to the Rx of the
ground reflection path. The fitted curves from the different heights of RSRP
measurements are shown in Fig. 15. It is observed that the fitted path loss
curves follow the measured RSRP property for all height measurements. We also
observe that the RSRP fluctuation from the ground reflected signal in the two-
ray path loss model clearly is captured in 30 m height, while the fluctuation
is small in 70 m and 90 m heights. This is because higher height reduces Tx
and Rx antenna gains of the ground reflection path.

##  VII Coherence Bandwidth Analysis

Figure 16: The coherence bandwidth depends on the 3D distance and the flight
height.

We calculate the coherence bandwidth during different flights using the
estimated channels in Section V and evaluate its dependency on link distance
and UAV height. Fig. 16 shows the change in the coherence bandwidth. We obtain
the coherence bandwidth every 20 ms and rearrange it according to the 3D
distance. Then, we apply the moving average with 1000 samples of the window
size. The coherence bandwidth is calculated by finding the frequency shift
where the frequency correlation coefficient becomes 0.9. The frequency
correlation coefficient is calculated as

$\displaystyle R(\Delta f)$
$\displaystyle=\frac{1}{M}\sum_{i=1}^{M}H(f_{i})H(f_{i}+\Delta f)^{\star},$
(7)

where $H(f)$ is the frequency domain channel response, $\Delta f$ indicates
the frequency shift, and M is the number of available samples. The obtained
frequency correlation coefficient $R(\Delta f)$ is normalized by $R(0)$.

Since we configure 72 subcarriers with 15 kHz subcarrier spacing, the maximum
coherence bandwidth is clipped by 1.08 MHz. We observe that the coherence
bandwidth decreases as the 3D distance increases. This is because the effect
of the multi-path coming from the ground reflection and other objects become
stronger as the distance increases. It is also observed that the coherence
bandwidth is slightly larger at higher flight heights, especially for small
and large link distance regions for this particular experiment.

##  VIII Shadowing Distribution and Spatial Correlation Analysis

Figure 17: Probability density function of the shadowing component at 70 m UAV
height where it is fitted by Gaussian and left-skewed Gaussian.

(a) Correlation of horizontal distance (b) Correlation of vertical distance

Figure 18: Correlation of horizontal and vertical distance where it is fitted
by bi-exponential and exponential functions

In this section, we analyze shadowing and spatial correlation in terms of
horizontal distance and vertical distance. We obtain the shadowing component
from measured RSRP by subtracting Tx power and two-ray path loss, which is
obtained from Section VI. Fig. 17 shows the probability density function (PDF)
of the shadowing component which is fitted by Gaussian and left-skewed
Gaussian distributions. It is observed that the shadowing component is better
fitted to Left-skewed Gaussian compared with Gaussian. The PDF of the skewed
Gaussian distribution is given by

$\displaystyle
f(x)=2\phi\left(\frac{x-\xi}{\omega}\right)\Phi\left(\alpha\left(\frac{x-\xi}{\omega}\right)\right),$
(8)

where $\phi(\cdot)$ and $\Phi(\cdot)$ indicate the PDF and the cumulative
distribution function (CDF) of Gaussian distribution, respectively. The
parameter $\alpha$ in (8) decides the skewness of the distribution.

We also analyze the spatial correlation as a function of the horizontal and
vertical distance between the locations that the RSRP is calculated. Since
height is fixed throughout a certain experiment, and the horizontal trajectory
is identical for experiments at different UAV altitudes, we can separately
generate vertical and horizontal correlation. The correlation between two
different location samples $w_{i}$, $w_{j}$ is calculated by

$\displaystyle
R_{i,j}=\frac{(w_{i}-\nu_{i})(w_{j}-\nu_{j})}{\sigma_{w,i}\sigma_{w,j}},$ (9)

where $\nu$, $\sigma_{w}$ denote the mean and the standard deviation of the
samples. Fig. 18 shows the correlation from the measurements, along with the
bi-exponential function fit for horizontal distance correlation and the
exponential function fit for the vertical distance correlation. The
exponential function correlation model is also known as the Gudmundson model
[21]. The bi-exponential function is expressed as

$\displaystyle R(d_{\rm h})=ae^{-b_{1}d_{\rm h}}+(1-a)e^{-b_{2}d_{\rm h}},$
(10)

where $b_{1}$, $b_{2}$ are the fitting parameters.

##  IX Conclusion

In this paper, we analyze the channel propagation of air-to-ground LTE links
using an experimental dataset of raw LTE I/Q samples at 3.51 GHz. We conduct a
measurement campaign by using SDR and GPS receivers mounted on UAVs in a rural
environment. The UAV sweeps across the experimental area following the
predefined fixed-height trajectories. The I/Q data is collected at altitudes
ranging from 30 m to 110 m, at identical horizontal trajectories. We process
the measured I/Q samples by using MATLAB LTE Toolbox and analyze the
synchronization, cell search, channel estimation, and RSRP measurement
procedures. We post-process the received signal power by fitting it with a
two-ray path loss model considering the Tx and the Rx antenna patterns. We
compare and analyze the results at different UAV heights to observe the
height-dependent channel characteristics. Our future work includes repeating a
similar study using the I/Q data from 5G wireless transmissions.

## Acknowledgement

This research is supported in part by the NSF award CNS-1939334 and its
associated supplement for studying NRDZs.

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# A Model-Agnostic Framework for Recommendation via Interest-aware Item
Embeddings

Amit Kumar Jaiswal  0000-0001-8848-7041 University of SurreyUnited Kingdom
<EMAIL_ADDRESS>and  Yu Xiong  University of SurreyUnited Kingdom
<EMAIL_ADDRESS>
(2023)

###### Abstract.

Item representation holds significant importance in recommendation systems,
which encompasses domains such as news, retail, and videos. Retrieval and
ranking models utilise item representation to capture the user-item
relationship based on user behaviours. While existing representation learning
methods primarily focus on optimising item-based mechanisms, such as attention
and sequential modelling. However, these methods lack a modelling mechanism to
directly reflect user interests within the learned item representations.
Consequently, these methods may be less effective in capturing user interests
indirectly. To address this challenge, we propose a novel Interest-aware
Capsule network (IaCN) recommendation model, a model-agnostic framework that
directly learns interest-oriented item representations. IaCN serves as an
auxiliary task, enabling the joint learning of both item-based and interest-
based representations. This framework adopts existing recommendation models
without requiring substantial redesign. We evaluate the proposed approach on
benchmark datasets, exploring various scenarios involving different deep
neural networks, behaviour sequence lengths, and joint learning ratios of
interest-oriented item representations. Experimental results demonstrate
significant performance enhancements across diverse recommendation models,
validating the effectiveness of our approach.

Recommender System, Capsule Network, Interest-aware, Embedding

††journalyear: 2023††copyright: rightsretained††conference: Seventeenth ACM
Conference on Recommender Systems; September 18–22, 2023; Singapore,
Singapore††booktitle: Seventeenth ACM Conference on Recommender Systems
(RecSys ’23), September 18–22, 2023, Singapore, Singapore††doi:
10.1145/3604915.3610660††isbn: 979-8-4007-0241-9/23/09††ccs: Information
systems Recommender systems††ccs: Computing methodologies Neural networks

##  1\. Introduction and Prior Work

The field of recommendation systems has witnessed significant advancements in
recent years, primarily due to the rapid progress of deep learning techniques.
Notable successes have been observed in various domains, including news
recommendation, e-commerce, video recommendation, and advertisement. In the
context of recommendation systems, it is essential to consider user
interaction behaviours as they convey valuable information about the user’s
interests. These behaviours indicate not only the individual items within the
sequences but also the user’s singular or multiple interests. Users often
exhibit diverse interests, engaging with products from distinct categories
such as sports, food, and clothing. Identifying these interests becomes
challenging as they are embedded within the interactive behaviours, rendering
direct capture of such (intrinsic) interests set forth a formidable task. The
primary objective in recommendation systems revolves around effectively
targeting the diverse interests of varied users. Existing deep learning models
focussed on click-through rate (CTR) prediction such as Wide and Deep models
(Cheng et al., 2016) combine the training of wide linear models and deep
neural networks, thereby harnessing the advantages of both memorisation and
generalisation for recommender systems as opposed to self-attention based
models (Cheng and Xue, 2021). Deep Crossing (Shan et al., 2016) with follow up
deep and cross models (Wang et al., 2017, 2021), Product-network in network
(PIN) (Qu et al., 2018), DeepFM (Guo et al., 2017), adaptive factorization
network (Cheng et al., 2020) and xDeepFM (Lian et al., 2018) aim to extract
low-order and explicit high-order features by incorporating a product layer,
and traditional factorization machine module. In the case of deep interest
network (DIN) (Zhou et al., 2018), an attention mechanism is employed to
enhance the pooling weights of similar items. However, the deep interest
evolution network (DIEN) (Zhou et al., 2019) introduces a sequential model to
capture the sequential characteristics instead of relying solely on item
embeddings as in DIN. DIEN utilises the hidden states of the GRU as inputs for
attention and employs attentional update GRU (AUGRU) as a replacement for the
conventional attention model. Overall, deep neural networks play a crucial
role in depicting user interests based on past user-item interactions (Pal et
al., 2020; Liu et al., 2020), utilising item embedding vectors. To address the
issue pertaining to the dispersion of user interests, a method known as the
dynamic routing capsule network (Sabour et al., 2017) proposes to mitigate the
limitations of traditional convolutional neural networks (CNNs), which
successfully achieves a more comprehensive understanding of the relationships
between objects (Edraki et al., 2020). Building upon the concept of dynamic
routing, (Li et al., 2019) propose the multi-interest network with dynamic
routing to handle the diverse interests of users in retrieval systems. This
method adopts a strategy of representing a single user using multiple vectors,
each encoding distinct aspects of the user’s diverse interests. By employing
this multi-vector representation approach, (Li et al., 2019) enhances the
capacity to capture and account for the various facets of user interests in
the retrieval process. Nonetheless, current representation learning methods
primarily concentrate on optimising the item-based mechanism that connects
user interactive behaviour sequences with candidate items. Consequently, they
overlook the crucial factor of label diffusion within each item present in the
user interactive behaviour sequences. This oversight weakens the impact of
user interests during the backpropagation phase of the training model.
Additionally, as network layers become deeper and the dimension of the
embedding layer increases, the introduction of new methods often necessitates
the redesign of the entire model architecture or the inclusion of additional
datasets or information. Hence, it becomes imperative to develop a method that
can enhance model performance while maintaining generalisability, eliminating
the need for extensive architectural modifications or the introduction of
supplementary information. In this work, a novel framework, Interest-aware
Capsule network (IaCN), is proposed that can effectively learn item
representations based on user interests. Our proposed framework directly
incorporates the concept of user interests to enhance the modelling process.
To ensure the generalisability of the framework, an auxiliary task to cater
users’ multiple interests based on (Pi et al., 2019) has been proven effective
in improving model performance. By jointly learning item-based and interest-
based item representations, the framework enables the integration of diverse
user interests into the entire model. Consequently, the interest-based item
representations generated by IaCN are shared with the original model, thereby
enriching the overall information captured by the model.
Our Contributions: To summarise, we make the following contributions:

  * •

We propose a novel framework, the User Interest-aware Capsule network, which
aims to facilitate the learning of interest-based item representations. By
incorporating IaCN as an auxiliary task, this framework can be seamlessly
integrated with existing recommendation models.

  * •

We propose an approach that combines a joint learning method and
hyperparameter optimisation within the IaCN framework. Through extensive
experimentation on benchmark datasets, our results demonstrate significant
improvements across various existing recommendation models.

##  2\. The Proposed Model

Within the context of recommendation systems, $i$ represents the
identification of an item, and the set of user interactive behaviour
sequences, represented by $\mathbb{S}_{u}$, encompasses the sequences of items
that a user $u$ has either clicked on or viewed. The symbol $u_{p}$
corresponds to the user’s basic profile information. The identification of a
candidate item $i_{t}$, while $r_{t}$ represents the associated information of
said candidate item, obtained from the recommendation system. The function
$f(i_{t},r_{t})$ represents the feature function that captures the
characteristics of the candidate item information derived from the combination
of the candidate item $i_{t}$ and the item-related information $r_{t}$. The
representation of user interests is typically performed to facilitate the
learning of the function $f(\mathbb{S}_{u},u_{p})$, which encompasses both the
user profile and the user’s interactive behaviour sequences. Therefore, the
formulation of user interests can be expressed as follows,

(1)
$\displaystyle\mathcal{I}_{u}=f(\mathbb{S}_{u},u_{p})\mid\mathcal{I}=(\mathrm{i}^{1}_{u},\mathrm{i}^{2}_{u},\ldots,\mathrm{i}^{K}_{u})\in\mathbb{R}^{h\times
K}$

where $\mathcal{I}$ denote the representation vector that is learned from the
user information $\mathbb{S}_{u}$ and $u_{p}$. The dimension of the user
interest vector is denoted by $h$, while $K$ represents the number of
dimensions in the user vector. Specifically, the vector $\mathrm{i}^{1}_{u}$
represents one of the multiple interest vectors of the user, and $\mathcal{I}$
represents the collection of these interest vectors.

Figure 1. A schematic architecture of the IaCN framework which serves as an
auxiliary task and shares item embeddings with the deep interest network.
Within the IaCN, user interactive behaviour sequences and user profiles are
incorporated and processed through a dynamic routing capsule mechanism. This
mechanism enables the extraction of user interests in the form of capsules. We
employ a scaled dot product between the interest capsules and candidate item
embeddings, these elements are brought into alignment within the same
representation space.

In addition, the representation of candidate items aims to learn the function
$f(i_{t},r_{t})$, which involves the item id and its associated information.
The candidate item embeddings can be obtained through
$c_{t}=f(i_{t},r_{t})\mid c_{t}\in\mathbb{R}^{h}$, where vector $c_{t}$
represents the embedding representation learned from the target item id
$i_{t}$ and its corresponding associated information $r_{t}$. Specifically,
the vector $c_{t}$ consistently refers to the vector obtained from the
embeddings and pooling layer in the Interest-aware Capsule Networks, as
discussed in the subsequent section. The purpose of a recommendation system is
to promote ‘desirable’ items to users navigating a website or web application.
Consequently, it is essential to establish a scoring mechanism that measures
the relationship between a candidate item and the user’s interests. This score
can be defined as
$\mathcal{R}_{\text{score}}(\mathcal{I}_{u},c_{t})=c^{T}_{t}\mathrm{i}^{K}_{u}$.
By calculating the value of $\mathcal{R}_{\text{score}}$, we can
quantitatively assess the proximity between user interests and candidate
items. Ultimately, the recommendation system will utilise the collection of
users multiple interests to identify and present the most desirable items to
the user.

###  2.1. Interest-aware Capsule Network

To overcome certain limitations associated with convolutional neural networks,
capsules have been introduced as an alternative that employs vector-based
representations instead of scalars to encode appearance features (Sabour et
al., 2017). This transition enables improved preservation of spatial
relationships among complete objects and their constituent parts.
Additionally, capsules incorporate the dynamic routing mechanism, which
enables the weighing of part contributions to the entire object in a varying
manner at each inference step. Users’ diverse interests are typically
concealed within their interactive behaviour sequences and profile
information. Capsules (Sabour et al., 2017) offer a means to encode appearance
feature representations by employing vectors instead of scalars, accomplished
through the assembly of a group of neurons. Through dynamic routing in capsule
networks, the weights of different capsules are learned, enabling the encoding
of relationships between parts and wholes. Notably, capsules demonstrate a
superior comprehension of object relationships compared to CNNs (Edraki et
al., 2020). In the domain of recommendation systems, multi-interest with
dynamic routing network (Li et al., 2019) leverages dynamic routing of
capsules to automatically capture high-level multiple user interests,
resulting in commendable performance within the e-commerce retrieval system.
Building upon this foundation, we propose an item representation approach
utilising user interest-aware capsule networks (IaCN) to enhance the
performance of CTR prediction models. We follow the notion of the dynamic
routing mechanism for Behaviour-to-Interest routing (Li et al., 2019),
employed by capsules for the purpose of learning multi-interest
representations based on user profile information and interactive behaviour
sequences. The input to each capsule,
$\mathrm{i}_{j}=\|s_{j}\|^{2}/(1+\|s_{j}\|^{2})(s_{j}/\|s_{j}\|)$, here,
$\mathrm{i}_{j}$ represents the output, while $s_{j}$ encompasses all the
inputs of capsule $j$. The formulation of components are,

(2) $\displaystyle s_{j}=\sum_{i,j}a_{ij}\hat{x}_{j\mid i}=\sum
a_{ij}W_{ij}x_{j},a_{ij}=\frac{\exp(b_{ij})}{\sum_{k}\exp(b_{ik})}$

Subsequently, by employing dynamic routing, the aim is to extract high-level
abstract interests from the raw user features. The function $a_{ij}$
represents the activation operation (Softmax) applied to the input $b_{ij}$.
The Behaviour to Interest approach selectively combines the user’s viewing
sequences into vectors representing multiple interests in an adaptive manner.
Based on the routing logits ($\mathcal{R}_{\text{score}}$), the variable
$b_{ij}$ can be expressed as, $b_{ij}=u^{T}_{j}Mc_{i}\mid i\in
I^{c}\forall\\{0,1,\ldots,n\\},j\in\\{0,1,\ldots,K\\}$, where
$c_{i}\in\mathbb{R}^{h}$ represents an embedding vector of item $i$ derived
from the user’s interactive behaviour sequences. The variable $v{c_{i}}$
denotes one specific item embedding vector from these sequences. Moreover,
$u_{j}\in\mathbb{R}^{h}$ refers to the capsule vector representing the user’s
interests, where $j$ ranges from 1 to $K$. The hyperparameter $K$ represents
the number of user interests. The matrix $M\in\mathbb{R}^{h\times h}$,
facilitates the bilinear mapping and serves as a linkage between the user’s
capsule interests and the viewed sequences. Meanwhile, $b_{ij}$ represents the
connection between the user’s interest and the item, ensuring they are mapped
to the same vector space. While multi-interest dynamic routing network (Li et
al., 2019) captures multi-interest capsule vectors from user interactive
behaviour sequences and profile information, it also introduces label-aware
attention using the scaled dot product to assess the relationship between user
interests and item information. Within the label-aware attention layer (as
shown in Figure 1), the candidate item serves as the query, the user interest
capsule acts as the key and value, and the candidate item embedding vector is
represented within the interest capsule space. The label-aware attention layer
incorporates the item embedding vector along with the item embeddings from the
original recommendation model to incorporate user-diverse, interest-based item
representations into the primary model. The primary model is specifically
designed to accommodate the requirements of the particular task at hand, such
as a ranking model (DIN and DIEN). The formulation of the scaled dot product
can be expressed as $\mathrm{i}_{u}=\mathcal{I}_{u}$
Softmax(pow($\mathcal{I}^{T}_{u}c_{i},p))$, where $p$ refers to the user
profile information. As a result, the probability $P(c_{i}\mid\mathrm{i}_{u})$
is derived, and the softmax activation function is applied to select the most
desirable option. The training loss is formulated as
$\mathcal{L}_{\text{IaCN}}=\sum_{u,i}\log P(c_{i}\mid\mathrm{i}_{u})$, where
$\mathcal{L}_{\text{IaCN}}$ represents the loss incurred by the user interest-
aware capsule network. It is crucial to ensure compatibility between the
user’s interests and the candidate item. To achieve this, the item embedding
vector and the user’s interest capsule vector are required to share a common
vector space, which is established based on the representation of the user’s
interests. This alignment is of significant importance when aiming to
construct interest-driven item representations.

###  2.2. Augmentation of Item Embeddings with User Interests

The embedding representation, which is based on deep learning, garners
significant attention in practical applications (Zhang et al., 2016; Naumov et
al., 2019; Cheng et al., 2016). Within the realm of recommendation systems,
each specific model utilises its unique method for generating embeddings. As
described in Section 1, several approaches have been introduced, focussing on
extracting multiple interests represented by item embeddings through the
design of network structures. Furthermore, the influence of label diffusion
into individual items within user interactive behaviour sequences diminishes
the strength of user interests during the backpropagation process of the
training model. While dynamic routing capsule networks can address this issue
to some extent, integrating them specifically into recommendation models
requires complex redesigning of the model architecture, rendering it a
challenging and infrequently utilised approach. Based on DIEN (Zhou et al.,
2019), the inclusion of an auxiliary task assumes a vital role in enhancing
model performance. To avoid the need for redesigning the intricate
architecture of the main recommendation model (DIN (Zhou et al., 2018)), an
auxiliary task is introduced to facilitate improved learning of item
representations. Consequently, we propose a framework known as the User
Interest-aware Capsule Network as an auxiliary task. Furthermore, the
interest-based item embeddings derived from IaCN are shared with DIN (Zhou et
al., 2018). Based on the formulation of $b_{ij}$, which employs the scaled dot
product to measure the distance among interests and items, the item embedding
vector is influenced by the user interest capsule vectors. Additionally, the
auxiliary task incorporates the item embeddings expressed through user
interest capsules into the main framework by sharing the item embedding
vector. Within the main framework (as delineated in Figure 1). The item
embeddings are illustrated as it consists of two distinct components,
$\mathbb{E}=\mathbb{E}_{\text{original}}\oplus\mathbb{E}_{\text{auxiliary}}$,
where the concatenation operator $\oplus$ is employed to combine the item
embedding vectors, denoted as $\mathbb{E}_{\text{original}}$ and
$\mathbb{E}_{\text{auxiliary}}$, respectively. The former corresponds to the
item embedding vector in the primary target recommendation model task, while
the latter is associated with the auxiliary task designed as IaCN. This
framework enables the expansion of the original model’s item embeddings by
incorporating user interest capsules, while maintaining the original model
architecture and preserving its inherent properties. As a result, this
framework can be seamlessly integrated into a wide range of general
recommendation models. The composition of the item embedding vector in the
main recommendation model involves a combination of the original model’s item
embeddings and the auxiliary model’s item embeddings. While the original item
embeddings remain unaffected by the auxiliary task, the auxiliary item
embedding is influenced by both tasks, with this influence being controlled
through a hyperparameter. Consequently, the overall loss function for the
entire model can be formulated as
$\mathcal{L}=\mathcal{L}_{\text{DIN}}+\lambda\mathcal{L}_{\text{IaCN}}$, where
$\mathcal{L}$ represents the overall loss of the complete model,
$\mathcal{L}_{\text{DIN}}$ denotes the loss of the main model (deep interest
network (Zhou et al., 2018), and $\mathcal{L}_{\text{IaCN}}$ corresponds to
the loss of the user interest-aware capsule network. The hyperparameter
$\lambda$ is used to fine-tune the balance between the loss and the auxiliary
task loss. Given $\mathcal{L}_{\text{IaCN}}$, the label-aware attention layer
necessitates positive samples for constructing the loss function.
Consequently, negative samples corresponding to label items are masked to
ensure that the loss function operates effectively during the training of the
complete model. Throughout the model training process, the item embedding
$\mathbb{E}$ receives two components of backpropagating gradients, one from
the main model and the other from the auxiliary model. The original item
embedding $\mathbb{E}_{\text{original}}$ solely receives the gradient from the
main model $g^{e}_{\text{main}}$, while the auxiliary item embedding
$\mathbb{E}_{\text{auxiliary}}$ receives gradients from both the auxiliary
model (IaCN) $g^{e}_{\text{main}}$ and the main original model
($g^{e}_{\text{auxiliary\\_main}}$) to facilitate its alignment
($\mathbb{E}_{\text{auxiliary}}$) with the main model. The update of the
auxiliary model gradient is performed according to the illustrated
formulation,

(3) $\displaystyle
g^{e}_{\text{auxiliary}}=(1-\delta)g^{e}_{\text{auxiliary\\_auxiliary}}+\delta
g^{e}_{\text{auxiliary\\_main}}\mid\delta\in[0,1]$

The selection of the appropriate hyperparameter $\delta$ is detailed in the
subsequent section.

##  3\. Experiments

This section presents the experimental evaluation of the user interest-aware
capsule network, which is introduced as an auxiliary task. The experiments are
conducted using a real-world dataset from Amazon, and we detail the results
and analysis in the following paragraphs.

###  3.1. Datasets

We employ the Amazon customer reviews, also known as product reviews dataset.
The dataset utilised in this study comprises 192,403 consumer reviews obtained
from Amazon electronics products, such as HDMI cables and Bluetooth speakers.
A total of 63,001 products from 801 categories were covered, resulting in a
dataset containing a total of 1,689,188 samples. The dataset encompasses
essential product information, including details about each product, its
corresponding category, and a list of viewed products. Another comprehensive
collection of consumer reviews from Amazon books, encompassing 603,668 reviews
in total are employed. These reviews pertain to 367,982 books, covering a wide
range of 1,600 categories. In addition to the consumer reviews, the dataset
provides essential product information, including details about the book’s
category and the list of viewed products.

###  3.2. Implementation Details and Results

We conduct experiments on the Amazon dataset encompassing electronics and
books, and compare the performance of our model with other extensively
employed baseline models within the industrial domain.
Baselines: We employ a set of baseline models for comparison, which are a)
Wide and Deep (Cheng et al., 2016), it integrates a deep neural network (DNN)
component as the ‘Deep’ part and a linear model component as the ‘Wide’ part.
This integration aims to leverage the strengths of both models, facilitating a
balance between model generalisation and memorisation. b) DIN (Zhou et al.,
2018) approach employs an attention mechanism to model the relationship
between user interactive behaviour sequences and candidate items, enabling the
determination of the contribution made by each item in the historical visiting
sequences to the target item. And, c) DIEN (Zhou et al., 2019) follows the
architectures of DIN, where this approach incorporates gated recurrent units
(GRUs) to effectively capture the evolutionary interests of users, while
leveraging attention mechanisms.

Table 1. Performance of individual models on the Amazon datasets. And, the
analysis of hyperparameter $\delta$ for DIEN with the auxiliary network (IaCN)
on Amazon books dataset.

Model Dataset | Books | Electronics
---|---|---
AUC | SD | AUC | SD
DIN (Zhou et al., 2018) | 0.7970 | 0.0010 | 0.7569 | 0.0009
DIN (Zhou et al., 2018)+IaCN | 0.8002 | 0.0009 | 0.7606 | 0.0013
Wide and Deep (Cheng et al., 2016) | 0.7860 | 0.0013 | 0.7461 | 0.0015
Wide and Deep (Cheng et al., 2016)+IaCN | 0.7928 | 0.0009 | 0.7502 | 0.0010
DIEN (Zhou et al., 2019) | 0.8534 | 0.0018 | 0.7706 | 0.0021
DIEN (Zhou et al., 2019)+IaCN | 0.8633 | 0.0019 | 0.7723 | 0.0002

DIEN (Zhou et al., 2019)+IaCN
---
$\delta$ | AUC | $\delta$ | AUC
0.1 | 0.862±0.0018 | 0.6 | 0.861±0.0014
0.2 | 0.864±0.0019 | 0.7 | 0.862±0.0010
0.3 | 0.864±0.0005 | 0.8 | 0.862±0.0018
0.4 | 0.864±0.0013 | 0.9 | 0.862±0.0012
0.5 | 0.861±0.0012 | 1.0 | 0.860±0.0022

Table 2. Performance with different user interactive behaviour sequences length $l$ on Amazon books Model | 10 | 20 | 50
---|---|---|---
DIEN (Zhou et al., 2019) | 0.854±0.0016 | 0.853±0.0018 | 0.850±0.0022
DIEN (Zhou et al., 2019)+IaCN | 0.857±0.0018 | 0.863±0.0019 | 0.861±0.0014

To explore the performance enhancements, we introduce an auxiliary network
(IaCN) that facilitates the extraction of multiple interests from the user’s
data. In this auxiliary network, we incorporate a layer of dynamic interest
capsules, denoted as $K=[\log_{2}|I^{c}_{u}|]$ to effectively extract the
user’s interests. The interest-based item embedding vector generated by the
IaCN model is shared with the primary recommendation model, and its gradient
is updated using a small ratio $\delta$=0.3. The overall loss is composed of
both the main model loss and the auxiliary model loss, with the hyperparameter
$\lambda$ set to 1 to appropriately weight the losses. The Adam optimizer is
employed with a learning rate of 1$e$-4. Each experiment is replicated five
times, and the standard deviation (SD) of the model’s using the area under the
curve (AUC) performance metric is computed to validate the effectiveness of
our proposed framework. Table 1 presents the AUC of various baseline models.
For the Amazon datasets, the maximum length of the list containing consumer-
viewed electronic products is 10, while the maximum length for consumer-viewed
books is 20. Notably, the framework incorporating the interest-aware capsule
network as an auxiliary component consistently enhanced the performance of all
model architectures, highlighting the advantages of this approach.
Importantly, the main recommendation model retains its original structure
while benefiting from the framework. Moreover, as the length of viewed items
increases, the framework exhibits improved efficacy, particularly in the case
of longer interactive behaviour sequences ($l$). To assess the performance of
our proposed framework relative to the benchmark, we conducted experiments
considering varying lengths of user-viewed sequences. The experimental results
are presented in Table 2, indicating that DIEN with IaCN outperforms the
original DIEN model in terms of AUC. Consequently, when dealing with long
click sequences from users, the user interests capsule network demonstrates
enhanced efficiency and accuracy in extracting diverse user interests, as
verified through various experimental setups.

##  4\. Conclusion and Future Work

In this work, we propose a comprehensive framework aimed at improving the
performance of recommendation models through the utilisation of interest-based
item embedding representations. To achieve this, a novel framework is
introduced, which directly learns interest-based item representations by
incorporating a user interest-aware capsule network. Furthermore, the user
interest-aware capsule network is incorporated as an auxiliary component,
facilitating the joint learning of both item-based item representations and
interest-based item representations within the framework. Our framework can be
readily applied to recommendation model architectures without the need for
additional data or significant modifications to the model structure.
Furthermore, the framework exhibits a notable impact on longer sequences of
viewed items and a wider range of user interests. Through experimental
evaluations on industrial datasets, it is evident that incorporating the user
interest-aware capsule network as an auxiliary component outperforms other
approaches in terms of click-through rate prediction tasks. Looking ahead, we
intend to extend this work towards developing an explainable recommendation
system that leverages the interest-aware capsule network to elucidate users’
interests.

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