# CCL4Rec: Contrast over Contrastive Learning for Micro-video Recommendation

Shengyu Zhang1∗, Bofang Li1∗, Dong Yao1∗, Fuli Feng4, Jieming Zhu6, Wenyan
Fan1, Zhou Zhao1,2,
Xiaofei He1, Tat-seng Chua5, Fei Wu1,2,3 1 Zhejiang University 2 Shanghai
Institute for Advanced Study of Zhejiang University  3 Shanghai AI Laboratory
4 University of Science and Technology of China 5 National University of
Singapore 6 Huawei Noah’s Ark Lab sy˙zhang, yaodongai, zhaozhou,
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
(2018)

###### Abstract.

Micro-video recommender systems suffer from the ubiquitous noises in users’
behaviors, which might render the learned user representation
indiscriminating, and lead to trivial recommendations (e.g., popular items) or
even weird ones that are far beyond users’ interests. Contrastive learning is
an emergent technique for learning discriminating representations with random
data augmentations. However, due to neglecting the noises in user behaviors
and treating all augmented samples equally, the existing contrastive learning
framework is insufficient for learning discriminating user representations in
recommendation. To bridge this research gap, we propose the Contrast over
Contrastive Learning framework for training recommender models, named CCL4Rec,
which models the nuances of different augmented views by further contrasting
augmented positives/negatives with adaptive pulling/pushing strengths, i.e.,
the contrast over (vanilla) contrastive learning. To accommodate these
contrasts, we devise the hardness-aware augmentations that track the
importance of behaviors being replaced in the query user and the relatedness
of substitutes, and thus determining the quality of augmented
positives/negatives. The hardness-aware augmentation also permits controllable
contrastive learning, leading to performance gains and robust training. In
this way, CCL4Rec captures the nuances of historical behaviors for a given
user, which explicitly shields off the learned user representation from the
effects of noisy behaviors. We conduct extensive experiments on two micro-
video recommendation benchmarks, which demonstrate that CCL4Rec with far less
model parameters could achieve comparable performance to existing state-of-
the-art method, and improve the training/inference speed by several orders of
magnitude.

Contrast over Contrast; Controllable Contrastive Learning; Micro-video
Recommendation

††copyright: acmcopyright††journalyear: 2018††doi:
10.1145/1122445.1122456††copyright: none††conference: Woodstock ’18: ACM
Symposium on Neural Gaze Detection; June 03–05, 2018; Woodstock,
NY††booktitle: Woodstock ’18: ACM Symposium on Neural Gaze Detection, June
03–05, 2018, Woodstock, NY††price: 15.00††isbn: 978-1-4503-XXXX-X/18/06††ccs:
Information systems Recommender systems

##  1\. Introduction

Micro-video online services, such as TikTok, Kuaishou, and Instagram, have
developed rapidly in recent years, leading to the proliferation of micro-video
production and communication. The tremendously grown volume of micro-videos
has intensified the need of retrieval and recommender systems that can permit
personalized content discovery and consumption in micro-video online services.
In the literature of modern recommender systems, the recent emergence of deep
learning techniques has provided enticing methods (Hidayati et al., 2018;
Jiang et al., 2019; Wang et al., 2020; Wei et al., 2019a, b; Gao et al., 2017;
Li et al., 2019; Chen et al., 2018; Jiang et al., 2020; Du et al., 2020; Li et
al., 2020; Wei et al., 2020; Yu et al., 2020; Yang et al., 2020; Lu et al.,
2020; Qian et al., 2022) in learning user representations that capture the
characteristics of users’ preferences and interests. More recently, in
frameworks that are specifically designed for micro-video recommendation, we
notice that Li et al., (Li et al., 2019) encapsulate the temporally remote
connections upon the behavior sequence based on visual similarity and devise
the temporal graph-based LSTM as the preference encoder. Jiang et al., (Jiang
et al., 2020) explicitly perform group routing and assignment to capture
group-level interest. Many of these works follow the sequential recommendation
schema (Huang et al., 2018, 2019; Rendle et al., 2010; Hidasi et al., 2016;
Zhou et al., 2018b), which typically makes predictions based on the historical
behavior sequence and a sequence modeling module.

Figure 1.  An illustration of the proposed contrast over contrast framework,
which explicitly models the hardness/quality $a$ of augmented samples and the
nuances between them to learn discriminating user representations.

Despite the fruitful progress in the literature, it is worth noting that there
are still some common challenges for micro-video recommendation. One of the
major challenges can be the sheer difficulty of learning discriminating and
effective user representations. Specifically, users behaviors are mostly
implicit feedbacks and the ubiquitous false-positive interactions (Wang et
al., 2020) may render the learned user representation indiscriminating (i.e.,
towards an average/over-smoothing representation of all users). Recently,
contrastive learning (Zhang et al., 2021; Zhang et al., 2022) has been
empirically successful at learning high-level features that are robust to low-
level noises (e.g., image rotation, flip). As illustrated in Figure 1, a
straightforward way to combine contrastive learning and recommendation is to
construct positive/negative behavior sequences via random
augmentation/sampling. Then a contrastive learning loss function, such as
InfoNCE (Oord et al., 2018), helps to distinguish the relative difference
between ¡query, positive¿ and ¡query, negative¿. However, such a
straightforward solution might be sub-optimal for recommendation due to the
following two problems: 1) random augmentations (e.g., item
drop/replacement/re-order) on the user behavior sequence treat all items
equally and cannot shield off the learned representation from false-positive
interactions within implicit feedbacks; and 2) existing contrastive learning
objectives that treat all positive/negative samples as equally important
prevent the framework from modeling the nuances between augmented samples and
thus making it inferior in learning discriminating representations.

To investigate the above challenges and effectively consolidate the merits of
contrastive learning into recommendation, we propose the Contrast over
Contrastive Learning framework for recommendation, abbreviated as CCL4Rec.
CCL4Rec follows the sequential recommendation schema to have the behavior
sequence as input. In essence, compared to existing contrastive learning
techniques that solely consider the contrast on ¡positive, negative¿ pairs,
CCL4Rec further considers the contrasts on ¡positive, positive¿, ¡negative,
negative¿ pairs. To make the contrast even more distinguishing, we enhance all
kinds of contrasts with adaptive pulling/pushing strengths that correlate with
the hardness/quality of augmented samples. In this way, CCL4Rec can learn to
capture the nuances between the augmented samples in a contrast over contrast
manner. To achieve such adaptive or controllable contrasts, the traditional
framework that constructs positive/negative views by random
augmentation/sampling can be less trackable and effective. Towards this end,
we propose to determine the hardness/quality of each augmented sample based on
the importance of replaced behaviors and the relatedness of negative
substitutes. The hardness scores permit not only adaptive pulling/pushing
strengths but also controllable contrastive learning by deliberately choosing
augmented samples with the desired hardness scores for contrasting. Therefore,
the CCL4Rec framework is capable of modeling the nuances between different
items within the historical behavior sequence (for the first problem), and the
nuances between different augmented samples (for the second problem).

In the experiments, we equip CCL4Rec with a naive sequence modeling module and
validate CCL4Rec on two large-scale micro-video recommendation benchmarks.
Quantitative experiments, including ablation studies, and in-depth
architecture analysis, demonstrate the strengths of CCL4Rec in being simple,
lightweight, and effective compared with existing state-of-the-art methods.
Remarkably, we show that CCL4Rec could achieve several orders of magnitude
improvement on the training/inference speed with comparable performance to the
state-of-the-art methods. In a nutshell, the contributions of this work are
listed as follows:

  * •

We propose to learn discriminating user representations for micro-video
recommendation via contrastive learning and further model the nuances of
augmented samples in a contrast over contrast manner.

  * •

We devise the novel CCL4Rec framework, which permits hardness-aware
augmentation with trackable hardness scores for augmented samples, adaptive
pulling/pushing strengths for different contrasting, and controllable
contrastive learning that can choose augmented samples with the desired
hardness scores for contrasting.

  * •

We conduct extensive experiments which demonstrate the strengths of CCL4Rec in
being simple, lightweight, and effective in learning discriminating user
representations.

##  2\. Related Works

###  2.1. Contrastive Learning

Recently, the contrastive learning objective has become advantageous for
unsupervised representation learning and led to state-of-the-art results in
various domains. The essence of contrastive learning lies in the infomax
(Linsker, 1988) principle, where mutual information of a query and its
positive views should be maximized, and the strategy of obtaining positive
pairs. Typical augmentation methods for computer vision (Chen et al., 2020)
include random cropping and flipping (Oord et al., 2018). Tian et al., (Tian
et al., 2020) also use different views of the same scene as positive samples.
Similarly, in natural language processing, Logeswaran and Lee (Logeswaran and
Lee, 2018) use the context sentences as the positive views of the query
sentence. Laskin et al., (Laskin et al., 2020) employ the contrastive
objective to improve sample efficiency of reinforcement learning. However,
most of the existing contrastive learning frameworks treat all
positive/negative samples as equally important, and we argue this can hinder
the learned representation from being discriminating. In this paper, we
propose to model the nuances between positives/negatives using contrast over
the traditional contrastive learning. Also, the proposed controllable
augmentation strategy constructs negatives with the desired hardness/quality
from the query sample, which empirically leads to robust training and better
performance.

Due to its effectiveness, there are increasing research interests on the
intersection of contrastive learning and recommendation (Zhang et al., 2021;
Zhang et al., 2022). It can be challenging to design augmentation strategies
for recommendation samples since we have neither easily accessible contexts
like the language nor straightforward (while effective) tools such as cropping
or rotating like the computer vision. Noteworthy, Zhou et al., (Zhou et al.,
2020a) directly treat items clicked by one user as the positive samples for
contrastive learning and demonstrate that such an objective can help reduce
popularity bias. Xie et al., (Xie et al., 2020) construct positive views for
one behavior sequence by cropping, masking, and reordering part of the
sequence, and treat the other randomly sampled behavior sequences as negative
samples. Their framework follows a typical pretraining strategy which
constitutes a pretraining stage and a fine-tuning stage. Zhou (Zhou et al.,
2020b) introduces the InfoNCE objective (Oord et al., 2018) into the self-
supervised masked item/attribute/segment prediction for sequential
recommendation. Liu et al., (Liu et al., 2021) propose a graph-based framework
that encapsulates graph perturbation, which can be viewed as the higher-order
version of the masking operation in (Xie et al., 2020) and a debiased
contrastive learning objective with items as positive/negative samples,
similar to (Zhou et al., 2020a). Different from these works, we propose a
hardness-aware augmentation strategy and construct positives/negatives
directly from the original behavior sequence. The proposed CCL4Rec framework
models the nuances between different behaviors and augmented samples, and thus
learns discriminating user representations in the embedding hypersphere.

###  2.2. Micro-video Recommendation

Micro-video recommendation is a nascent research area in content-based
recommender systems (Xun et al., 2021) and attracts increasing research
interests (Liu and Chen, 2019; Liu et al., 2019; Chen et al., 2018) recently
due to the booming of micro-video production and communication on the internet
(Zhang et al., 2020a, b). Typically, Li et al., (Li et al., 2019) propose to
model the dynamic and multi-level interests using the temporal graph-based
LSTM and a multi-level interest modeling layer. Wei et al., (Wei et al.,
2019b) explicitly model the modality-specific user preferences and use graph
neural networks to leverage inter-dependence between users and micro-videos in
multiple modality-specific graphs. Hao et al., (Jiang et al., 2020) devise the
time-aware parallel masks for leveraging multi-scale time effects and the
group routing algorithm to assign historical behaviors to different groups.
Different from these works, we propose to learn discriminating user
representations using contrastive learning, which remains largely unexplored
in the literature of both generic and micro-video recommendations.

##  3\. Methods

###  3.1. Overview

We now present the contrast over contrastive learning framework for
recommendation, termed CCL4Rec. The critical ideas of CCL4Rec are illustrated
in Figure 2. CCL4Rec follows a schema of sequential recommendation, i.e.,
explicitly modeling the historical behavior sequence for prediction. Such a
schema has the advantage of dynamically leveraging newly interacted items for
prediction without model re-training. CCL4Rec mainly encapsulates three
components: 1) Hardness-aware Augmentation, which determines the importance of
each historical behavior and the relatedness of each substitute. By jointly
considering the importance and relatedness, we conduct replacement
augmentation and obtain positive/negative user behavior sequences of various
hardness scores. Controllable Augmentation means we can obtain augmented
samples with the desired hardness scores. Based on the scores, we can
explicitly choose augmented samples of specific range of hardnesses along with
the contrastive learning processes, i.e., controllable contrastive learning.
Such scores further permit contrast over contrastive learning. 2) Sequence
Modeling, which encodes the original behavior sequence and the augmented
sequences into vectorial representations. We adopt a simplified design for
efficiency. 3) Contrast over Contrast, which explicitly models the nuances of
different augmented samples. We achieve this by designing hardness-related
objectives and contrasting either negative or positive samples besides the
conventional ¡query, positive, negative¿ contrasting schema. Furthermore,
based on the hardness of augmented samples, we learn from simple contrasts at
the early stage of training and progressively learn from harder contrasts,
which empirically leads to a more robust training process and better
performance.

###  3.2. Behavior/Substitute Ranking

Let $v$ and $u$ denote a micro-video and an user, and let the bold symbols
$\mathbf{v}$ and $\mathbf{u}$ denote the corresponding vectorial
representations. We denote the behavior sequence of $u$ as
$X_{u}=\\{v_{u,t}\\}_{t=1}^{N_{u}}$, where $v_{u,t}$ denotes the $t$th micro-
video interacted by user $u$ and $N_{u}$ is the number of behaviors in the
sequence. Since the following description is within the range of one user, we
write $v_{t}$ in place of $v_{u,t}$ for brevity. Given $X_{u}$, we propose to
determine the importance of each element $v_{t}$ in representing the interests
of user $u$, i.e., behavior importance ranking. We firstly compute the
relevance of each element with other items in the behavior sequence, i.e.,

(1)
$\displaystyle\alpha_{ij}=f_{a}(v_{i},v_{j})=(\mathbf{v}_{i}\mathbf{W}_{1})^{T}(\mathbf{v}_{j}\mathbf{W}_{2}),$

where $\mathbf{W}_{1}$ and $\mathbf{W}_{2}$ are trainable linear mapping
matrices. We propose to sum the relevance scores to all items and treat the
summed score as the importance for representing the user, i.e.,

(2) $\displaystyle\alpha_{t}=f_{s}(v_{t})=\sum_{j=1}^{N_{u}}\alpha_{tj},$

Intuitively, when a micro-video is more relevant to other micro-videos watched
by the user, it has a higher chance of being an important one. The computation
of importance introduces trainable parameters, i.e., $\mathbf{W}_{1}$ and
$\mathbf{W}_{2}$. Since we have no groundtruth for the importance score, we
encapsulate these parameters into the sequence modeling module and jointly
optimize them by the final cross-entropy loss function. We note that the
proposed contrastive objectives that explicitly rely on the importance scores
will not optimize $\mathbf{W}_{1},\mathbf{W}_{2}$, and we achieve this simply
using the detach() function in pytorch when computing the importance scores.
We will illustrate the objectives and training in Section 3.5 in detail.

For user $u$, all other items not in the behavior sequence will be treated as
potential substitutes for replacement augmentation. To accommodate the
hardness-aware augmentation described in Section 3.3, we propose to rank these
substitutes and compute the relatedness scores, i.e., substitute relatedness
ranking. However, it can be infeasible to rank the whole item gallery, which
can easily reach a billion-scale in real-world recommender systems. Towards
this end, we propose to approximate the above process and choose to rank a
subset of randomly sampled substitutes, i.e.,
$Z_{u}=\\{v^{z}_{k}\\}_{k=1}^{N_{z}}$, where $v^{z}_{k}$ is a potential
substitute and $N_{z}$ is the number of sampled substitutes to be ranked. For
user behaviors, we compute the importance score at the user level, and for
substitutes, we compute the relatedness score at a fine-grained behavior
level:

(3)
$\displaystyle\beta_{tk}=f_{b}(v_{t},v_{k}^{z})=(\mathbf{v}_{t}\mathbf{W}_{1})^{T}(\mathbf{v}_{k}^{z}\mathbf{W}_{2}),$

where $\beta_{tk}$ denotes the relatedness of substitute $v^{z}_{k}$ and
behavior $v_{t}$. We reuse the scoring matrices $\mathbf{W}_{1}$ and
$\mathbf{W}_{2}$ to reduce parameters.

Figure 2.  Schematic of the proposed CCL4Rec architecture, which mainly
encapsulates three critical contributions: 1) Hardness-aware augmentation,
that explicitly tracks the importance of behaviors and relatedness of
substitutes in user behavior sequence augmentation, resulting in a hardness
score of the augmented sample. 2) Contrast over contrast objective that
explicitly models the nuances between positives/negatives based on the
hardness scores. 3) Controllable contrastive learning that can deliberately
choose augmented samples with the desired hardness scores.

###  3.3. Hardness-aware Augmentation

Based on the behavior importance scores and substitute relatedness scores, we
propose to determine the hardness of augmented sequences. The term hardness is
borrowed from the term hard negative mining and refers to the quality of the
mined samples w.r.t. the query sample. We note that we largely refrain from
mining hard negatives from all other samples by augmenting the current sample
to have positives/negatives. Augmentation has the potential to be efficient,
controllable, and of high quality by manipulating samples at a fine-grained
component level. We conduct replacement augmentation, i.e.,
$X^{z}_{u}=(X_{u}\setminus\bar{X}_{u})\cup\bar{Z}_{u}$ by replacing $N_{r}$
micro-videos $\bar{X}_{u}$ with substitutes $\bar{Z}_{u}$ since replacement is
more trackable as compared to other alternatives such as sequence re-order.

Constructing Negatives.  When we prefer to replace items with high relative
importance score $\frac{\exp\left(\alpha_{i}\right)}{\sum_{v_{j}\in
X_{u}}\exp\left(\alpha_{j}\right)}$, the constructed behavior sequences are
potential negatives. We compute the hardness score for a potential negative
$X^{z,-}_{u}$ as follows:

(4) $\displaystyle
a^{-}=\sum_{(v_{m},v_{n}^{z})}^{N_{r}}\frac{\exp\left(\alpha_{m}\right)}{\sum_{v_{i}\in
X_{u}}\exp\left(\alpha_{i}\right)}*\frac{\exp\left(\beta_{mn}\right)}{\sum_{v_{j}^{z}\in
Z_{u}}\exp\left(\beta_{mj}\right)},$

where $v_{m}\in\bar{X}_{u}$ is a replaced behavior and
$v_{n}^{z}\in\bar{Z}_{u}$ is the corresponding substitute. $\alpha_{m}$ and
$\beta_{m,n}$ denote the importance score of $v_{m}$ for user $u$ and the
relatedness score of substitute $v_{n}^{z}$ for $v_{m}$. We use softmax to
transform the absolute importance and relatedness into the relative ones
within the range of the behavior sequence and the substitute subset,
respectively. The intuition behind the multiplication of relative importance
and relatedness scores for constructing negatives is that replacing an
important item with a highly related substitute will make the augmented
negative harder. For example, when a user loves watching the cat micro-videos,
replacing a cat micro-video in the behavior sequence with a dog micro-video
(of high related score) will make the augmented behavior sequence much harder
than replacing it with a cooking micro-video (of low related score). We
essentially treat all items the user does not click as negatives, which is a
common strategy in training deep candidate generation models for
recommendation. Also, replacing unimportant behaviors will intuitively lead to
easier augmented samples.

Constructing Positives.  When we prefer to replace items with high relative
unimportance score $\frac{\exp\left(-\alpha_{i}\right)}{\sum_{v_{j}\in
X_{u}}\exp\left(-\alpha_{j}\right)}$, the constructed behavior sequences are
potential positives. We compute the hardness score for $X^{z,+}_{u}$ as
follows:

(5) $\displaystyle
a^{+}=\sum_{(v_{m},v_{n}^{z})}^{N_{r}}\frac{\exp\left(-\alpha_{m}\right)}{\sum_{v_{i}\in
X_{u}}\exp\left(-\alpha_{i}\right)}*\frac{\exp\left(-\beta_{mn}\right)}{\sum_{v_{j}^{z}\in
Z_{u}}\exp\left(-\beta_{mj}\right)}.$

The difference between positives and negatives lies in that we use the
negative importance/relatedness score before softmax, which will result in a
relative unimportance/unrelatedness score after softmax. The intuition behind
the multiplication of relative unimportance and unrelatedness scores for
constructing positives is that replacing an unimportant item with an unrelated
substitute might make the augmented positive be of high quality. For example,
when a user dislikes ads micro-videos, replacing an ads micro-video in the
behavior sequence with a micro-video that is far from ads (of low related
score) will have a higher chance of making the positive be of high quality
than replacing it with another ads micro-video (of high related score).
Replacing important behaviors will intuitively make the constructed positives
be of low quality.

Based on the above computation, we can devise controllable augmentation by
deliberately choosing the behaviors to be replaced and the substitutes to
construct positives/negatives samples with the desired hardness scores, i.e.,
hardness-aware augmentation. The controllable augmentation permits many
enticing contrastive techniques compared with the vanilla contrastive
learning, of which the details are discussed in 3.5.

###  3.4. Sequence Modeling

Sequence modeling lies in the core of most sequential recommendation
architectures, which transform the historical behavior sequence into a
holistic (or multiple) user interest representation. To better demonstrate the
effectiveness of the proposed framework, we propose to simplify the sequence
modeling and introduce just one linear transformation layer $\mathbf{W}_{3}$
with NO further trainable parameters. This design largely improves the
efficiency of both training and inference, which is a critical merit for
industrial systems. Specifically, we obtain the original user representation
as:

(6) $\displaystyle\mathbf{\hat{v}}_{t}$
$\displaystyle=\sum_{i=1}^{N}\left(\mathbf{v}_{i}\mathbf{W}_{3}\right)*\frac{\exp\left(\alpha_{ti}\right)}{\sum_{v_{j}\in
X_{u}}\exp\left(\alpha_{tj}\right)},$ (7) $\displaystyle\mathbf{u}^{q}$
$\displaystyle=\sum_{t=1}^{N}\mathbf{\hat{v}}_{t}*\frac{\exp\left(\alpha_{t}\right)}{\sum_{v_{i}\in
X_{u}}\exp\left(\alpha_{i}\right)}.$

where $\mathbf{v}_{i}$ is updated by aggregating information from all other
micro-video in the behavior sequence by re-using the correlation score
$\alpha_{ti}$ as the weights after softmax. The final user representation
$\mathbf{u}^{q}$ sums all micro-video features by re-using the importance
score $\alpha_{t}$ as the weights after softmax. This architecture has the
strengths of being simple: efficient for training/serving, lower chance of
overfitting, and less space complexity. Augmented behavior sequences
$\\{X^{z,+}_{i}\\}_{i=1}^{N_{p}},\\{X^{z,-}_{j}\\}_{j=1}^{N_{n}}$ are also
transformed into the corresponding user representations
$\\{\mathbf{u}^{+}_{i}\\}_{i=1}^{N_{p}},\\{\mathbf{u}^{-}_{j}\\}_{j=1}^{N_{n}}$.

###  3.5. Training and Objectives

####  3.5.1. Contrast on Users

Based on the augmented positive and negative user representations, we conduct
contrastive learning to learn discriminating user representations. A
straightforward way is to employ the margin triplet loss function (Balntas et
al., 2016):

(8)
$\displaystyle\sum_{i=1}^{N_{p}}\sum_{j=1}^{N_{n}}\max\left(d\left(\mathbf{u}^{q},\mathbf{u}^{+}_{i}\right)-d\left(\mathbf{u}^{q},\mathbf{u}^{-}_{j}\right)+\delta,0\right),$

where $d(\cdot)$ denotes the distance measurement and $\delta$ denotes the
margin, which indicate the maximum relative distance between ¡original,
positive¿ and ¡original, negative¿.

Contrast over Contrastive Learning.  However, the above formulation basically
treats all positives and negatives as equally important. Failing to model the
nuances between them might hinder contrastive learning from learning
discriminating user representations. Towards this end, we propose to: 1) model
the hardness of positives and negatives in the modeling; and 2) further
contrast the negatives or positives in a contrast over contrast manner.
Mathematically, the hardness can be incorporated into the objective as
follows:

(9) $\displaystyle\delta^{*}$
$\displaystyle=\max(\min((a^{+}_{i}+a^{-}_{j})*\delta^{s},\delta^{u}),\delta^{l}),$
(10) $\displaystyle\mathcal{L}_{ccl}$
$\displaystyle=\sum_{i=1}^{N_{p}}\sum_{j=1}^{N_{n}}\max\left(d\left(\mathbf{u}^{q},\mathbf{u}^{+}_{i}\right)-d\left(\mathbf{u}^{q},\mathbf{u}^{-}_{j}\right)+\delta^{*},0\right),$

where $a^{+}_{i},a^{-}_{j}$ denote the hardness scores of augmented samples
$\mathbf{u}^{+}_{i},\mathbf{u}^{-}_{j}$, computed in Equation 5 and Equation
4, respectively. Intuitively, if a positive is of high quality, we should pull
the corresponding user representation $\mathbf{u}^{+}_{i}$ closer to the
original $\mathbf{u}^{q}$. Similarly, if a negative is of high hardness, we
should push $\mathbf{u}^{-}_{j}$ farther from the original. Therefore, the
maximum relative distance, i.e., the margin $\delta$ should be larger to
permit further contrasting than the other pairs. We use the sum of harness
score $a^{+}_{i}+a^{-}_{j}$ to control the margin and employ hyper-parameters
$\delta^{s},\delta^{u},\delta^{l}$ to control the scale, upper bound, lower
bound of the final margin. Besides the conventional way of contrasting
positives with negatives, we further contrast positives or negatives as
follows:

(11) $\displaystyle\delta^{+,*}$
$\displaystyle=\max(\min((a^{+}_{i}-a^{+}_{k})*\delta^{s},\delta^{u}),\delta^{l}),$
(12) $\displaystyle\mathcal{L}_{ccl}^{+}$
$\displaystyle=\sum_{i=1}^{N_{p}}\sum_{a^{+}_{k}<a^{+}_{i}}\max\left(d\left(\mathbf{u}^{q},\mathbf{u}^{+}_{i}\right)-d\left(\mathbf{u}^{q},\mathbf{u}^{+}_{k}\right)+\delta^{+,*},0\right),$

Unlike $\mathcal{L}_{ccl}$, we use $a^{+}_{i}-a^{+}_{k}$ to control the
margin. Intuitively, when contrasting positives, if the qualities of two
augmented samples largely differ from each other, the maximum relative
distance should be large. Similarly in spirit, we could compute
$\mathcal{L}_{ccl}^{-}$ by contrasting the negatives. We note that the above
objectives that explicitly rely on the hardness scores will not optimize the
score-related parameters.

Controllable Contrastive Learning.  As demonstrated in Section 3.3, with the
computation of hardness scores, we can easily have hardness-aware
augmentation, which further permits controllable contrastive learning by
deliberately choosing augmented samples of the desired hardness scores for
contrasting. In this paper, we propose multiple controllable contrast
strategies and discuss their empirical performance in Section 4.3.1. We take
the easy2hard strategy as an example for illustration. With the easy2hard
strategy, we propose to learn from augmented samples with smaller hardness
scores at the beginning and increase the hardness scores of the chosen samples
as the learning procedure processes. Specifically, when constructing
positives/negatives, we filter the half items with the largest/smallest
importance scores $\alpha_{i}$, and further sample half items from the
filtered set to be replaced, i.e., $\bar{X}_{u}$. The sampling probabilities
are initially set to the relative unimportance scores, i.e.,
$\frac{\exp\left(-\alpha_{i}\right)}{\sum_{v_{j}\in
X_{u}}\exp\left(-\alpha_{j}\right)}$, which means we construct positives with
lower hardness scores at the early stage of training. The sampling
probabilities will be linearly transformed towards the importance scores
$\frac{\exp\left(\alpha_{i}\right)}{\sum_{v_{j}\in
X_{u}}\exp\left(\alpha_{j}\right)}$ along with the training processes.
Similarly, we sample substitutes according to the unrelatedness scores
$\frac{\exp\left(-\beta_{ik}\right)}{\sum_{v_{j}^{z}\in
Z_{u}}\exp\left(-\beta_{ij}\right)}$, which will be linearly transformed
towards the relatedness score along with the training. Controllable negative
augmentation shares similar processes as the above.

####  3.5.2. click-through-rate prediction.

As a common practice, we incorporate a multi-layer perceptron (MLP) to predict
the CTR and a cross-entropy loss as the objective:

(13) $\displaystyle\hat{y}$
$\displaystyle=\operatorname{MLP}\left(\left[\mathbf{u},\mathbf{v}_{i}\mathbf{W}_{4},\mathbf{u}*\mathbf{v}_{i}\mathbf{W}_{4}\right]\right),$
(14) $\displaystyle\mathcal{L}_{ce}$
$\displaystyle=-\sum_{\hat{y},y^{*}}y^{*}\log\left(\hat{y}\right)+\left(1-y^{*}\right)\log\left(1-\hat{y}\right),$

where $\left[\cdot\right]$ denotes the concatenation operation and $*$ denotes
element-wise product. We also compute the corresponding cross-entropy loss for
augmented samples. While $\mathcal{L}_{ce}^{+}$ for positives share the same
computation, $\mathcal{L}_{ce}^{-}$ is computed with the groundtruth
$y_{ui}^{*}$ reverted, i.e., $1\rightarrow 0$, and disregards target micro-
videos with label 0.

####  3.5.3. Contrast on User-Item Pairs

Besides contrasting users, we also consider the contrast between the user and
positive/negative items. The objective function can be written as:

(15)
$\displaystyle\mathcal{L}_{cui}=-\sum_{u}\log\frac{\sum_{\hat{y}_{u},y_{u}^{*}}y_{u}^{*}\exp\left(\hat{y}_{u}\right)}{\sum_{\hat{y}_{u},y^{*}_{u}}y^{*}_{u}\exp\left(\hat{y}_{u}\right)+\sum_{\hat{y}_{u},y^{*}_{u}}(1-y^{*}_{u})\exp\left(\hat{y}_{u}\right)},$

This contrast is an essential complement to the original cross-entropy loss by
globally enlarging the gap between the summed predictions of positive pairs
and that of negative pairs.

####  3.5.4. Training

During training, we naively sum the above loss functions and do not tune the
relative weights with bells and whistles to better demonstrate the strengths
of the proposed objectives:

(16)
$\displaystyle\mathcal{L}=\mathcal{L}_{ccl}+\mathcal{L}_{ccl}^{-}+\mathcal{L}_{ccl}^{+}+\mathcal{L}_{ce}+\mathcal{L}_{ce}^{+}+\mathcal{L}_{ce}^{-}+\mathcal{L}_{cui}.$

##  4\. Experiments

We are concerned with the following research questions:

  * •

RQ1: How does CCL4Rec perform compared with the state-of-the-art micro-video
recommenders?

  * •

RQ2: Do all contrastive objectives contribute to the effectiveness of CCL4Rec?
How do different key hyper-parameters settings and controllable contrastive
strategies affect the performance?

  * •

RQ3: Does CCL4Rec learn discriminating user presentations?

###  4.1. Experimental Settings

Table 1. Statistics of the Datasets. Dataset | #Users | #Items | #Interactions | #Density
---|---|---|---|---
MicroVideo-1.7M | 10, 986 | 1, 704, 880 | 12, 737, 619 | 0.068%
Kuaishou | 10, 000 | 3,239,534 | 13, 661, 383 | 0.042%

Datasets  As a common practice of micro-video recommendation research (Jiang
et al., 2020; Li et al., 2019), we conduct experiments on two micro-video
datasets, i.e., MicroVideo-1.7M (Chen et al., 2018) and Kuaishou 111
https://www.kesci.com/home/competition/5ad306e633a98340e004f8d1.. These two
datasets both provide pre-extracted video features and logged interactions in
the form of ¡User ID, Video ID, Timestamp¿. Each interaction is labeled as
positive or negative according to whether the user clicked the exposed video
when he/she saw the thumbnail. We exactly follow the data pre-processing of
Chen et al., (Chen et al., 2018), and Li et al., (Li et al., 2019) for a fair
comparison with the existing state-of-the-art models. The statistics of these
datasets are listed in Table 1.

Evaluation Protocals  To conduct a comprehensive evaluation on CCL4Rec and
multiple comparison methods, we employ multiple numerical metrics, i.e., Area
Under Curve (AUC), Precision, Recall, and F1-score, which are widely used in
micro-video recommendation (Jiang et al., 2020; Li et al., 2019) and can
reveal the effectiveness of models in multiple aspects. Specifically,
Precision indicates the fraction of recommended items that are actually
clicked by users among the number of recommended items in total while Recall
cares those among the number of clicked items in total. F1 score is computed
as the harmonic mean of Precision and Recall, and thus taking these two
metrics into account. AUC indicates the probability of a randomly sampled item
that is actually clicked being ranked higher than a randomly sampled item that
is not. We report the results that are computed based on the Top 50
recommended items by all models, i.e., Precision@50, Recall@50, and
F1-Score@50.

Table 2. Overall performance comparison between CCL4Rec and micro-video recommendation baselines. We highlight the best/next-best performance with bold/underline style. We show that CCL4Rec achieves comparable performance to the state-of-the-art methods but is of far more efficiency as illustrated in Table 3. | MicroVideo-1.7M | Kuaishou
---|---|---
Model | AUC | Precision@50 | Recall@50 | F1-Score@50 | AUC | Precision@50 | Recall@50 | F1-Score@50
BPR | 0.583 | 0.241 | 0.181 | 0.206 | 0.595 | 0.290 | 0.387 | 0.331
LSTM | 0.641 | 0.277 | 0.205 | 0.236 | 0.713 | 0.316 | 0.420 | 0.360
CNN | 0.650 | 0.287 | 0.214 | 0.245 | 0.719 | 0.312 | 0.413 | 0.356
NCF | 0.672 | 0.316 | 0.225 | 0.262 | 0.724 | 0.320 | 0.420 | 0.364
ATRank | 0.660 | 0.297 | 0.221 | 0.253 | 0.722 | 0.322 | 0.426 | 0.367
THACIL | 0.684 | 0.324 | 0.234 | 0.269 | 0.727 | 0.325 | 0.429 | 0.369
ALPINE | 0.713 | 0.300 | 0.460 | 0.362 | 0.739 | 0.331 | 0.436 | 0.376
MTIN | 0.729 | 0.317 | 0.476 | 0.381 | 0.752 | 0.341 | 0.449 | 0.388
CCL4Rec | 0.722 | 0.312 | 0.472 | 0.376 | 0.750 | 0.340 | 0.449 | 0.387
Table 3. Computation Complexity Analysis. Training time concerns one epoch and testing concerns one batch. We choose two best-performing state-of-the-art methods and all models are with batch size of 32 and 1 NVIDIA V100 GPU. We use the official codebases of ALPINE and MTIN provided by the authors and run them with default settings on the Kuaishou dataset. We highlight the speedup rate over two state-of-the-art methods. | Training (s) | Inference (s) | #Parameters
---|---|---|---
ALPINE | 10,778.50 | 0.7191 | 208,854,467
MTIN | 2,896.83 | 3.2070 | 10,849,706
CCL4Rec | 121.88 | 0.0076 | 758,341
Efficiency |  88.44/23.8$\times$ |  94.6/463.4$\times$ | 275.4/14.3$\times$

Implementation Details  We use Adam optimizer (Kingma and Ba, 2015) for
training CCL4Rec. All experiments are with batch size 32 and learning rate
0.003. We use weight normalization at a rate of $1e-7$. We randomly sample
10,000 micro-videos from the whole gallery as potential substitutes shared by
the users in one batch. i.e.. $N_{z}=10,000$. We disregard the trainable user
embedding layer in our experiment. Micro-video embeddings are 128-dimensional
vectors. We tune $\delta^{l},\delta^{u}$ among $\\{0.5,0.6,0.7,0.8,0.9\\}$ and
$\\{1.1,1.2,1.3,1.4,1.5\\}$, respectively, and empirically choose the ones
that achieve best results. We choose $\delta^{u}$ that can roughly map most
scores into the range of $[\delta^{l},\delta^{u}]$. For a fair comparison with
the state-of-the-art micro-video recommender, i.e., MTIN (Jiang et al., 2020),
which uses pretrained user interest embeddings in their experiment, we also
incorporate the embeddings provided by the authors for better performance.
Specifically, there are six user interest embeddings for each user, and we
model them using the sequence modeling module described in Section 3.4 with
separate linear transformation matrices. We also model the micro-videos that
are recommended to users but not watched by them as an additional sequence
using the proposed sequence modeling module, following ALPINE (Li et al.,
2019) and MTIN (Jiang et al., 2020). We note that the proposed augmentations
are not performed on these additional sequences, and we obtain the final CTR
prediction by the average of the predictions from multiple sequences.

###  4.2. Comparison Methods

To demonstrate the effectiveness of CCL4Rec, we consider the following state-
of-the-art recommenders as comparison methods.

  * •

BPR (Rendle et al., 2009). Bayesian personalized ranking devises the maximum
posterior estimator from the Bayesian perspective. The BPR loss is computed as
the relative difference between positive pairs and negative pairs.

  * •

LSTM (Zhang et al., 2014). An LSTM based sequential recommender. We use the
final hidden state as the user interest vector and an MLP predictor for click-
through-rate prediction.

  * •

CNN. CNN extracts features from the behavior sequence, and we take the pooled
feature as the user interest vector. We use various kernels with various
window sizes to extract features from the behavior sequence and apply global
pooling on the extracted features to obtain user interest vector. Similarly,
we use an MLP predictor for prediction.

  * •

NCF (He et al., 2017). NCF is a collaborative filtering recommender equipped
with deep neural networks, which can be of great representation power compared
with the traditional inner product.

  * •

ATRank (Zhou et al., 2018a). ATRank is a ranking recommender that
comprehensively leverages the power of attention mechanisms.

  * •

THACIL (Chen et al., 2018). THACIL divides the historical behaviors into
multiple temporal blocks and captures both the intra-block and inter-block
correlations. In addition, THACIL considers both category-level and item-level
user interests for representing users’ interests.

  * •

ALPINE (Li et al., 2019). The essence of ALPINE lies in the temporal graph-
based LSTM module, which captures dynamic interests within the temporal
behavior graph, and the multi-level interest modeling layer, which models
multi-type behaviors.

  * •

MTIN (Jiang et al., 2020). To investigate the multi-scale time effects and
item-to-interest grouping problem, MTIN devises the time-aware parallel masks
and the group routing algorithm.

We report the evaluation results of all methods on two micro-video
recommendation benchmarks in Table 2. Since computation complexity is one of
the critical factors considered by industrial recommender systems, we also
report the training/inference/#parameters of the strongest state-of-the-art
methods (i.e., ALPINE, MTIN) and CCL4Rec in Table 3. By analyzing these
results, we have the following observations:

  * •

Although CCLRec cannot beat the strongest state-of-the-art method, i.e., MTIN,
in most cases, it achieves comparably good results and outperforms many other
existing micro-video recommendation state-of-the-art methods. In particular,
the relative improvement over other simple designs (e.g., BPR, LSTM, CNN, NCF)
and also some advanced approaches (e.g., ATRank, THACIL) reach nearly 100% and
8% with Recall@50 on the MicroVideo and Kuaishou benchmarks, respectively. We
attribute these advantages to the following aspects: 1) The contrastive
learning objective helps the sequence modeling module to learn more
distinguishing user representations that can mitigate the effects of
ubiquitous false-positive interactions by contrasting the learned
representation with augmented samples. 2) The proposed hardness-aware
augmentation considers the importance of replaced items and the relatedness of
substitutes, and accordingly determining the hardness scores of augmented
samples in a fine-grained manner. These designs help to model the nuances of
different items in augmentation, and the nuances of different augmented
samples in contrasting, leading to even more distinguishing user
representations.

  * •

While achieving competitive performance, CCL4Rec significantly improves the
training efficiency (88.44/23.8 times faster than ALPINE/MTIN) and the
inference efficiency (94.6/463.4 times faster than ALPINE/MTIN). These results
demonstrate the strengths of CCL4Rec in being simple and effective, which are
critical for industrial scenarios where the number of users/items can easily
reach billions in scale. Furthermore, the number of parameters is orders of
magnitude (275.4/14.3) less than the other two state-of-the-art methods.
Considering the rather simplified sequence modeling module in the CCL4Rec
framework, CCL4Rec has the potentials to enhance a tiny-size model that will
be used for on-device/edge computing.

###  4.3. Study of CCL4Rec (RQ2)

Table 4. Analysis on the number of augmented positives/negatives. $N_{p},N_{n}$ | AUC | Precision@50 | Recall@50 | F1-Score@50
---|---|---|---|---
2 | 0.7425 | 0.3386 | 0.4469 | 0.3852
3 | 0.7471 | 0.3402 | 0.4491 | 0.3871
4 | 0.7503 | 0.3401 | 0.4487 | 0.3869
5 | 0.7487 | 0.3397 | 0.4479 | 0.3863
Table 5. Analysis on different contrastive learning objectives. Model | AUC | Precision@50 | Recall@50 | F1-Score@50
---|---|---|---|---
CCL4Rec | 0.7503 | 0.3401 | 0.4487 | 0.3869
\- $\mathcal{L}_{ccl}^{-/+}$ | 0.7388 | 0.3359 | 0.4439 | 0.3824
\- $\mathcal{L}_{ccl}$ | 0.7273 | 0.3353 | 0.4428 | 0.3816
\- $\mathcal{L}_{cui}$ | 0.7239 | 0.3327 | 0.4404 | 0.3790

####  4.3.1. Study of the controllable contrastive strategies.

To demonstrate the merits of controllable contrastive learning empowered by
the controllable augmentation, we devise multiple strategies as follows:

  * •

The harder strategy, which means we prefer to choose the augmented samples
with larger hardness scores. We achieve this by using importance/relatedness
scores as weights to sample the micro-videos to be replaced and the
substitutes;

  * •

The easier strategy, which means we prefer ones with smaller hardness scores.
We achieve this by using unimportance / unrelatedness scores as weights.

  * •

The easy2hard strategy as illustrated in Section 3.5;

  * •

The hard2easy strategy; and 5) The random strategy as a widely used baseline.

From the training curve depicted in Figure 3, we have the following
observations: 1) compared to the baseline random strategy that has several
high loss/performance jitters, other controllable contrastive strategies
overall have significantly less and lower jitters; 2) among all the
contrastive strategies, the easy2hard strategy has the most robust training
process and yields the best performance in most times. We note that $random$
strategy can be the most straightforward one that is widely used in many
contrastive learning based methods. However, according to our analysis, it
might lead to unstable training process and more training epochs to converge.
These results jointly demonstrate the necessity of controllable contrastive
learning and the effectiveness of our design in boosting performance and
training robustness.

(a) Training loss

(b) Testing Precision

Figure 3.  Training curves (training loss and testing precision) of different
controllable contrastive strategies.

####  4.3.2. Study of the number of augmented positives/negatives.

We vary the number of augmented positives/negatives to reveal its effect on
the performance. The results in Table 4 indicate that: 1) the performance of
CCL4Rec is overall insensitive to this hyper-parameter and small $N_{p},N_{n}$
will lead to competitive performance; 2) increasing $N_{p},N_{n}$
($2\rightarrow 3,4$) will yield performance improvement since the contrast
over contrast objective should learn more distinguishing user representation
by modeling the nuances between multiple positives/negatives; 3) Further
increasing $N_{p},N_{n}$ ($3\rightarrow 4$) will lead to fewer gains. The
reason for this might be that moderate $N_{p},N_{n}$ is enough to reach the
optimal, which can be computationally efficient.

####  4.3.3. Study of the contrastive objectives. (Ablation Study)

The critical contributions of CCL4Rec are the contrastive objectives. To
evaluate whether each of them contributes to the final performance, we ablate
CCL4Rec by progressively removing them and test the resulting architectures.
Specifically, we observe the following from Table 5: 1) By removing
$\mathcal{L}_{ccl}^{-/+}$, which means we disregard the contrasts between
positives or negatives, the performance gap between the resulting model and
the full model demonstrates the necessity and effectiveness of the contrast
over contrast framework; 2) By further removing $\mathcal{L}_{ccl}$, i.e.,
eliminating the effect of contrasting the learned representation with positive
and negative ones, we can observe a clear performance drop. It is noteworthy
that $\mathcal{L}_{ccl}$ also exhibits a contrast over contrast merit since
the adaptive pulling/pushing strengths help CCL4Rec model the nuances between
different augmented samples; 3) further removing $\mathcal{L}_{cui}$, which
means the CCL4Rec framework totally lost the contrastive learning capability.
The performance drop further shows the merits of the proposed contrastive
learning framework.

###  4.4. Case Study (RQ3)

To evaluate whether we learn discriminating user representations via contrast
over contrastive learning, we follow a widely used case study schema (Wang et
al., 2019; Wei et al., 2020) to visualize the t-SNE transformed embeddings. We
adopt a contrastive baseline that is trained with $\mathcal{L}_{ce}$ and
$\mathcal{L}_{ccl}$ without adaptive pulling/pushing strengths, i.e., the
architecture without contrast over contrast. We plot two randomly sampled
users and each user has four augmented positives/negatives with the hardness
scores predicted by the corresponding model. As shown in Figure 4, we have the
following observations:

  * •

Both results yield clear gap between positives and negatives, which reveal the
effectiveness of contrastive learning.

  * •

The positives/negatives are less distinguishable from each in the baseline
hyper-embedding space. Since positives and negatives are obtained by
transforming important/unimportant items in the behavior sequence, these
results basically indicate that the contrastive baseline treat all items as
equally important and thus learning indiscriminating representations.

  * •

By further considering the hardness scores, CCL4Rec successfully pushes
negatives with high hardness scores farther and pulls positives with high
hardness scores closer (while the baseline can not). These results demonstrate
the effectiveness of contrast over contrast objective in CCL4Rec.

(a) Contrastive baseline

(b) Contrast over Contrast

Figure 4.  Case study with learned t-SNE transformed representations derived
from the contrastive baseline and our CCL4Rec framework. Black/red/blue nodes
represent the query/positive/negative samples. Each augmented sample is with a
hardness score predicted by the corresponding model.

##  5\. Conclusion and Future Work

In this work, we investigate the problem of learning discriminating user
representation via contrastive learning. Different from existing contrastive
learning approaches, we propose to better accommodate recommendation using
hardness-aware augmentation and contrast over contrastive learning. By
hardness-aware augmentation, we can easily obtain augmented samples with the
desired hardness scores by deliberately manipulating the importance of
behaviors being replaced and the relevance of substitutes. Based on the
hardness scores, we devise the controllable contrastive strategies, contrasts
on negatives or positives, and contrast with adaptively pulling/pushing
strengths, which jointly help to model the nuances between different augmented
samples and thus learning discriminating representations.

To the best of our knowledge, the contrast over contrastive learning framework
is among the earlest attempts in research on recommendation, while also
contributing to the literature of generic contrastive learning. We believe the
insights of CCL4Rec are inspirational to future developments on learning
effective user representations and may be beneficial to a broad range of
research domains. We plan to further explore the strengths by designing better
augmentation cost prediction modules and better objectives to model the
nuances of augmented samples. Another future direction is to take a more in-
depth analysis of controllable contrastive learning. We mainly analyze the
effects on the training process and final performance. Carefully designed
strategies and broader merits are ripe for exploration. Some previous works
found that the contrastive learning objective can be beneficial for bias
reduction. We thus plan to also explore whether the contrast over contrastive
learning framework can further help to reduce biases in recommender systems
(e.g., popularity bias) compared with the traditional one.

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