For methods that model news recommendation as a regression task (e.g., predict
the ratings of news), several common metrics for regression such as mean
absolute error (MAE), mean squared error (MSE), rooted mean squared error
(RMSE) and Pearson correlation coefficient (PCC) are used to indicate the
recommendation performance, which are respectively formulated as follows:

(5) $\displaystyle\rm{MAE}$
$\displaystyle=\frac{1}{N}\sum_{i=N}|r_{i}-p_{i}|,$ (6)
$\displaystyle\rm{MSE}$
$\displaystyle=\frac{1}{N}\sum_{i=N}(r_{i}-p_{i})^{2},$ (7)
$\displaystyle\rm{RMSE}$
$\displaystyle=\sqrt{\frac{1}{N}\sum_{i=N}(r_{i}-p_{i})^{2}},$ (8)
$\displaystyle\rm{PCC}$
$\displaystyle=\frac{1}{N-1}\sum_{i=1}^{N}(\frac{r_{i}-\bar{r}}{\sigma_{r}})(\frac{p_{i}-\bar{p}}{\sigma_{p}}),$

where $r_{i}$ and $p_{i}$ are the real and predicted ratings of the $i$-th
sample, $\bar{r}$ and $\bar{p}$ respectively denote the arithmetic mean of the
real and predicted ratings, and $\sigma$ is the standard deviation.

For methods that regard news recommendation as a ranking task, besides the AUC
metric there are also several other metrics such as Average Precision (AP),
Hit Ratio (HR), Mean Reciprocal Rank (MRR) and normalized Discounted
Cummulative Gain (nDCG). Note that these metrics may be applied to the top K
recommendation lists, e.g., HR@K and nDCG@K. These metrics are respectively
formulated as follows:

(9) $\displaystyle\rm{AP}$
$\displaystyle=\frac{1}{N_{p}}\sum_{i=1}^{N_{p}}\frac{|\\{k|Rank(p_{k})\leq
Rank(p_{i})\\}|}{Rank(p_{i})},$ (10) $\displaystyle\rm{HR@K}$
$\displaystyle=\frac{|\\{k|Rank(p_{k})\leq K\\}|}{K},$ (11)
$\displaystyle\rm{MRR}$
$\displaystyle=\frac{1}{N_{p}}\sum_{i=1}^{N_{p}}\frac{1}{Rank(p_{i})},$ (12)
$\displaystyle\rm{nDCG@K}$
$\displaystyle=\frac{\sum_{i=1}^{K}(2^{r_{i}}-1)/\log_{2}(1+i)}{\sum_{i=1}^{N_{p}}1/\log_{2}(1+i)},$

where $r_{i}$ is a relevance score of news with the $i$-th rank, which is 1
for clicked news and 0 for non-clicked news. There are several other metrics
such as Click-Through Rate (CTR) and dwell time, which can be only used to
measure the performance of online news recommenders.

Besides the metrics for measuring ranking accuracy, there are several other
objective or subjective metrics to evaluate news recommender systems in other
aspects. In (Gabrilovich et al., 2004) the recommendation results are
evaluated by novelty, which is subjectively judged by a group of human
subjects by rating the news sets from most novel to least novel. In FeedRec
(Wu et al., 2021d), the recommendation results are further evaluated by a set
of user engagement-related metrics, such as the average dwell time, finish
ratio, dislike ratio and share ratio of the top ranked news. These metrics can
help comprehensively evaluate the performance of news recommender systems and
further improve user experience. A few methods also measure the diversity of
recommendation results in different aspects. For example, in (Zheng et al.,
2018), an Intra-List Similarity (ILS) function is used to measure the
diversity of recommendation results. More specifically, given a ranking list
$L$, its ILS score is calculated as follows:

(13) $ILS(L)=\frac{\sum_{b_{j}\in L}\sum_{b_{j}\in L,b_{j}\neq
b_{i}}S(b_{i},b_{j})}{\sum_{b_{j}\in L}\sum_{b_{j}\in L,b_{j}\neq b_{i}}1},$

where $S(b_{i},b_{j})$ represents the cosine similarity between the item
$b_{i}$ and $b_{j}$. A similar diversity metric ILAD is also used in (Qi et
al., 2021b, d). SentiRec (Wu et al., 2020d) uses a set of sentiment diversity
metrics to measure the sentiment difference between historical clicked news
and candidate news, which are formulated as follows:

(14) $\displaystyle Senti_{MRR}$
$\displaystyle=max(0,\bar{s}\sum_{i=1}^{N}\frac{s^{c}_{i}}{i}),$
$\displaystyle Senti@5$ $\displaystyle=max(0,\bar{s}\sum_{i=1}^{5}s^{c}_{i}),$
$\displaystyle Senti@10$
$\displaystyle=max(0,\bar{s}\sum_{i=1}^{10}s^{c}_{i}),$

where $N$ is the number of candidate news in an impression, $s^{c}_{i}$
denotes the sentiment score of the $i$-th ranked candidate news.

Besides diversity, several fairness metrics are used to measure whether a news
recommender system is fair to different groups of users or different news
publishers. For example, FairRec (Wu et al., 2021i) uses the accuracy of
sensitive attribute (e.g., gender) prediction based on top recommendation
results as the fairness metric, where a higher accuracy means more serious
unfairness because the recommendation results are more heavily influenced by
sensitive attributes. (Gharahighehi et al., 2021) studies the news
recommendation fairness to different groups of authors. It uses an Equity
Attention for group fairness (EAGF) measurement and a Supplier Popularity
Deviation (SPD) measurement for evaluating such kind of fairness, which if
formulated as follows:

(15) $\displaystyle EAGF$ $\displaystyle=\sum_{i=1}^{|g|}\sqrt{|L(i)|},$
$\displaystyle SPD$
$\displaystyle=\frac{\sum_{i=1}^{|g|}|\frac{|L(i)|}{|L|}-\frac{|A(i)|}{|A|}|}{|g|},$

where $g$ is the set of author groups and $L(i)$ is the set of recommended
news belonging to the $i$-th group, $L$ is the set of all recommended items,
$A(i)$ is the set of items in the training set belonging to the $i$-th group,
and $A$ is the whole set of items. A higher EAGF and a lower SPD score
indicate better fairness. These metrics used in the two works can be used to
measure user-side fairness and provider-side fairness, respectively.

With the development of privacy-preserving news recommendation methods based
on federated learning, a few measurements can be used to evaluate the degree
of privacy protection in news recommendation. For example, in FedRec (Qi et
al., 2020) the privacy protection ability of the model can be directly
indicated by the privacy budget of model gradients. In addition, privacy
protection can also be measured by conducting membership inference attack on
user behavior histories to guess whether a behavior belongs to a target user
(Qi et al., 2021c). These metrics can indicate whether private user
information encoded in exchanged models results is well-protected.

##  8\. Dataset, Competition and Benchmark

Many works in the news recommendation field are based on proprietary datasets,
such as those collected from Google News (Das et al., 2007), Yahoo’s news
(Okura et al., 2017), Bing news (Lian et al., 2018) and MSN news (Wu et al.,
2019c). There are only a few publicly available datasets for the research on
personalized news recommendation, which are respectively introduced as
follows.

The first one is the plista (Kille et al., 2013) dataset. It is constructed by
collecting the 70,353 news articles from 13 German news portals as well as
1,095,323 news click logs of users. In the CLEF 2017 NewsREEL task, the
organizers publish a new version of the plista dataset, which records users’
interactions with news from eight publishers in February 2016. This dataset
contains 2 million notifications, 58 thousand news updates, and 168 million
recommendation requests. The language used in the plista datasets is German
since it is mainly based on the news websites and users in German speaking
world. Note that the number of users is not provided.

The second one is the Adressa (Gulla et al., 2017) dataset, which was
constructed by collecting the news logs of the Adresseavisen website in three
months. It has a full version with logs in 10 weeks and a small version with
logs in one week. The small version contains 561,733 users, 11,207 articles
and 2,286,835 clicks, and the full version contains 3,083,438 users, 48,486
articles and 27,223,576 clicks. The news articles in Adressa are written in
Norwegian.

The third one is the Globo (de Souza Pereira Moreira et al., 2018) dataset,
which is retrieved from the Globo news portal in Brazil. This dataset contains
about 314,000 users, 46,000 news articles and 3 million news clicks. This
dataset is in Portuguese, and there is no original news text in this dataset,
and it only provides the embeddings of words generated by a neural model that
is pre-trained in a news metadata classification task.

The fourth one is a
Yahoo!333https://webscope.sandbox.yahoo.com/catalog.php?datatype=l dataset for
session-based news recommendation. It contains 14,180 news articles and 34,022
click events. In this dataset, no news text is provided and the number of
users is also unknown because there is no information about user ID.

The fifth one is the MIND (Wu et al., 2020a)444https://msnews.github.io/
dataset, which is a large-scale English dataset for news recommendation. This
dataset is recently released by MSN News, which contains the real news logs of
1 million users in 6 weeks (from October 12 to November 22, 2019). It involves
161,013 news articles, 15,777,377 impressions and 24,155,470 news clicks.

We present a comparison of the volume, textual information and leaderboard
information of these datasets in Table 6. We can see that only the MIND
dataset is associated with a public leaderboard. In fact, many researches
conducted on other datasets such as Adressa use different dataset
preprocessing methods (Zhu et al., 2019; Hu et al., 2020a), making it
difficult to make head-to-head comparisons between the results reported in
different papers. On the contrary, on the MIND dataset the training,
validation and test samples are given, and the evaluation metrics are
consistent. Thus, MIND can serve as a standard testbed for news recommendation
research.

Table 6. Comparisons of the five public datasets for news recommendation.

Dataset | Language | # Users | # News | # Clicks | News Text | Has Leaderboard?
---|---|---|---|---|---|---
Plista | German | Unknown | 70,353 | 1,095,323 | title, body | $\times$
Adressa | Norwegian | 3,083,438 | 48,486 | 27,223,576 | title, body, category, entities | $\times$
Globo | Portuguese | 314,000 | 46,000 | 3,000,000 | word embeddings of texts | $\times$
Yahoo! | English | Unknown | 14,180 | 34,022 | anonymized word IDs | $\times$
MIND | English | 1,000,000 | 161,013 | 24,155,470 | title, abstract, body, category, entities | $\checkmark$
|  |  |  |  |  |

Based on the datasets introduced above, several competitions and benchmarks on
personalized news recommendation have been established. One representative one
is the NEWSREEL challenge held from 2013 to 2017 (in 2013 the challenge is
named NRS).555https://www.newsreelchallenge.org/ There are usually two tasks
in the NEWSREEL challenge. The first one is news recommendation in a living
lab, which are conducted on an operating news recommendation service. The goal
of recommendation algorithms in this task is achieving high news CTRs. The
second one is offline evaluation of news recommendation methods in a simulated
environment. This task is performed based on the plista dataset, and the goal
is to predict which news articles a visitor would read in the future. In the
2017 edition of NewsREEL 87 participants are registered (Kille et al., 2017),
and two systems achieved CTRs higher than 2% in the online evaluation task.

Another recent competition is the MIND News Recommendation
Competition666https://msnews.github.io/competition.html, which is conducted on
the MIND dataset. The goal of this challenge is to predict the click scores of
candidate news based on user interests and rank candidate news in each
impression. This challenge attracted more than 200 registered participants and
the top submission achieved 71.33% in terms of AUC. The leaderboard of this
challenge opens after the challenge, and researchers can submit their
predictions on the test set to obtain the official evaluation scores. The
current top result on this leaderboard is 73.04% in terms of AUC, which is
achieved by a recommender named “UniUM-Fastformer-Pretrain” based on the
techniques in (Wu et al., 2021c) and (Wu et al., 2021h). The MIND dataset,
challenge and the public leaderboard can form a good benchmark to facilitate
research and engineering on personalized news recommendation.

##  9\. Responsible Personalized News Recommendation

Although personalized news recommendation techniques have achieved notable
success in targeting user interest, they still have several issues that may
affect user experience and even lead to potential negative social impacts.
There are several critical problems in developing more responsible
personalized news recommender systems, including privacy protection, debiasing
and fairness, diversity, and content quality, which are discussed in the
following sections, respectively.

###  9.1. Privacy Protection

Most existing personalized news recommender systems rely on centralized
storage of users’ behavior data for user modeling and model training. However,
user behaviors are usually privacy sensitive, and centrally storing them may
lead to users’ privacy concerns and further risks on data leakage (Li et al.,
2020). There are only a few works that study the privacy preservation problem
in news recommendation (Desarkar and Shinde, 2014; Qi et al., 2020). For
example, FedRec (Qi et al., 2020) may be the first attempt to learning
privacy-preserving news recommendation model. Instead of collecting and
storing user behavior data in a central server, in FedRec users’ news click
data are locally stored on user devices. FedRec uses a federated learning
based framework to collaboratively learn news recommendation model. Each
client keeps a local copy of the model and locally computes the model updates
based on local data. The local model updates are uploaded to a central server
that coordinates a number of user clients for model training. The server
aggregates the local gradients into a global one to update its maintained
global model, and distributes the updated global model to user devices for
local update. In addition, to further protect user privacy, FedRec applies
local differential privacy (LDP) techniques to perturb the local model
gradients. Since the protected model gradients usually contain much less
private information, user privacy can be better protected. However, FedRec is
only a framework for privacy-preserving news recommendation model training,
and privacy-preserving online serving is still a challenging problem.

Uni-FedRec (Qi et al., 2021c) is an improved version of FedRec that considers
both privacy-preserving training and serving. It has a recall stage to
generate dozens of candidates from the news pool in the server and a ranking
stage to locally rank candidate news. In the recall stage, Uni-FedRec
generates multiple user embeddings to better cover user interest. Instead of
sending the original user embedding learned by the user model, it decomposes
each user embedding into a linear combination of several basis user
embeddings, and the combination weights are protected by LDP before sending to
the server. The server reconstructs user embeddings to retrieve candidate news
and send them to clients for local ranking. This framework can be used for
both model training and serving in a privacy preserving way. However, there
are still considerable communication costs in this framework. Efficient-FedRec
(Yi et al., 2021b) further studies how to reduce the communication costs of
federated news recommendation model learning. It decomposes the whole model
into a heavy news model and a light-weight user model, where the news model is
placed on the server while the user model is kept by clients. The hidden news
representations inferred by the news model on the server are distributed to
clients in the model training. This method provides the potential of
incorporating big models such as BERT in federated news recommendation.

Although existing works on privacy-preserving news recommendation have made
notable progresses, there are still many challenges in this field, such as the
huge performance sacrifice of differential privacy mechanism, the difficulty
of involving some context features (e.g., CTR) and collaborative information
in GNN, and the difficulty of real-world deployment of federated news
recommender systems.

###  9.2. Debiasing

User behavior data usually encodes various kinds of biases. Some kinds of
biases are related to news. For example, click behaviors are influenced by the
positions and sizes of news displayed on the webpages (i.e., presentation
bias) (Yi et al., 2021a). In addition, popular news may have higher chances to
be clicked than unpopular news (i.e., popularity bias) (Qi et al., 2021b).
These types of bias information may affect the accuracy of user interest
modeling and model training. A few works explore to eliminate the influence of
certain kinds of bias information to improve personalized news recommendation.
For instance, DebiasRec (Yi et al., 2021a) aims to reduce the influence of
position and size biases on news recommendation. It uses a bias-aware user
modeling method to learn debiased user interest representations, and uses a
bias-aware click prediction method that decomposes the overall click score
into a bias score and a bias-independent user preference score. PP-Rec (Qi et
al., 2021b) uses a popularity-aware user modeling method to learn calibrated
user interest representations, and it separately models the popularity of news
and users’ personal preference on news, which can help better model
personalized user interest. These methods mainly aim to infer debiased user
interest from biased user data. However, without any prior knowledge about
unbiased data distribution, the bias information usually cannot be fully
eliminated. In addition, many kinds of bias such as exposure and selection
biases are rarely studied in the news recommendation field. Thus, it is
important for future research to understand how different biases affect user
behaviors and the recommendation model as well as how to eliminate their
effect in model training and evaluation.

###  9.3. Fairness

Making fair recommendations is an important problem in responsible news
recommendation. Researchers have studied various kinds of fairness problems in
recommendation, such as provider-side fairness and consumer-side fairness
(Burke, 2017). In personalized news recommendation, a representative kind of
unfairness is brought by the biases related to sensitive user attributes, such
as genders and professions. Users with the same sensitive attributes may have
similar patterns in news click behaviors, e.g., fashion news are more
preferred by female users. The model may capture these biases and produce
biased recommendation results, e.g., tend to only recommend fashion news to
female users. This will lead to the unfairness problem that some users cannot
obtain their interested news information, which is harmful to user experience.
To address this problem, FairRec (Wu et al., 2021i) uses a decomposed
adversarial learning framework with independent user models to learn a bias-
aware user embedding and a bias-free user embedding. The bias-aware user
embedding mainly aims to capture bias information related to sensitive user
attributes, and the bias-free user embedding aims to model bias-independent
user interest. Both embeddings are regularized to be orthogonal thereby the
bias-free user embedding can contain less bias information. The bias-free user
embedding is further used for making fair news recommendations. By learning
user embeddings that are agnostic to the sensitive user attributes, the
unfairness brought by the bias information related to sensitive user
attributes can be effectively mitigated. However, adversarial learning based
methods are usually brittle and it is difficult to tune their hyperparameters
to fully remove the bias information. In addition, many other genres of
fairness (e.g., provider-side fairness) are less studied in news
recommendation. In summary, there are many types of fairness to be improved in
news recommendation and it is non-trivial to make both fair and accurate news
recommendations.

###  9.4. Diversity

Diversity is critical for personalized news recommendation (Reuver et al.,
2021; Liu et al., 2021b; Prawesh and Padmanabhan, 2021). Users may not prefer
to click news with homogeneous information and improving the information
variety is important for improving user experience and engagement (Bernstein
et al., 2020). However, most existing news recommendation methods focus on
optimizing recommendation accuracy while ignoring recommendation diversity,
and it is shown in (Wu et al., 2020d; Qi et al., 2021b, d) that many existing
news recommendation methods cannot make sufficiently diverse recommendations.
There are only a few methods that consider the diversity of news
recommendation. Some methods aim to recommend news that are diverse from
previously clicked news (Gabrilovich et al., 2004; Wu et al., 2020d), and
several other works explore to diversify the top news recommendation list (Li
et al., 2011b; Gabriel De Souza et al., 2019). However, there is still no work
on promoting both kinds of diversity in news recommendation. In addition, many
diversity-aware news recommendation methods rely on reranking strategies to
improve recommendation diversity, which may not be optimal for achieving a
good tradeoff between recommendation accuracy and diversity. Thus, further
research on learning unified diversity-aware news recommendation models is
important for improving the quality of online news services.

###  9.5. Content Moderation

The moderation of news content in news recommendation is a rarely studied
problem. In fact, some news articles published online are clickbaits, fake
news or containing misinformation. In addition, some news may encode
adversarial clues (Descampe et al., 2021) or contain low-quality or even
harmful content (e.g., racialism and hate speech). Recommending these news
will damage user experience and the reputation of news platforms, and may even
lead to negative societal impact (Lazer et al., 2018). Although online news
platforms can perform manual moderation on news content quality, the huge
amount of online news information makes it too difficult or even impossible to
filter all news articles with harmful and useless content. Thus, it is
important to design news recommendation algorithms that can avoid recommending
news with low-quality content. Researchers have found that news with high
ratios of short reading dwell time (e.g., less than 10 seconds) are probably
clickbaits (Wu et al., 2020b). In addition, user behaviors such as comments
and sharing on social media may also provide rich clues for detecting news
that contain misinformation and harmful content (Shu et al., 2017; Banko et
al., 2020). Thus, incorporating the various user feedback has the potential to
help recommend news with high-quality content, which can improve the
responsibility of news recommendation algorithms.

##  10\. Future Direction and Conclusion

By comprehensively reviewing existing news recommendation techniques in
different aspects, we can see that personalized news recommendation techniques
have achieved substantial progress over the past years. However, there remain
many challenges and unresolved problems. Thus, in this section we raise
several potential directions that worth exploring in the future.

###  10.1. Deep News Understanding

News modeling is at the heart of personalized news recommendation. It can be
improved in the following aspects. First, text understanding is a core problem
in news modeling, and existing methods may not be capable of understanding the
textual content of news deeply. Thus, using more advanced NLP techniques
(e.g., knowledge-aware PLMs) may help better understand news texts and improve
news modeling. Second, besides textual information, news also contain rich
multimodal information such as images, videos and slides. The multimodal news
content can provide complementary information on news understanding. Thus,
using multimodal content modeling techniques has the potential to improve the
comprehensiveness of news understanding. Third, there are many useful factors
for news modeling that are not covered by news content, such as publisher,
popularity and recency. A unified framework is required to incorporate various
kinds of news information (e.g., property features and context features) and
meanwhile effectively model the relatedness between different features.
Further research on these directions can help understand news more accurately
and deeply to empower subsequent user modeling and news ranking.

###  10.2. Universal User Modeling

User modeling is critical for understanding users’ interest in news. However,
it is difficult to model the dynamic and diverse user interest accurately and
comprehensively for news recommendation. To tackle this problem, a universal
user modeling framework that can model various kinds of user interest is
needed. We argue that this framework should satisfy the following
requirements. First, the user modeling framework needs to comprehensively
infer user interest from multiple kinds of user behaviors and feedback. This
is because click behaviors are very noisy and may be sparse for some users,
and it is insufficient to model user interests solely from click behaviors.
Fortunately, different kinds of user behaviors and feedback (e.g., read and
dislike) can provide rich complementary information like user engagement, and
incorporating them in a unified framework can better support user modeling.
Second, the framework needs to model the diverse and multi-grained user
interest. Since a single user embedding may be insufficient to comprehensively
model user interests, it may be a promising way to represent user interest
with more sophisticated structures such as embedding sets and graphs to
improve the understanding of user interest. Third, the framework needs to
capture the dynamics of user interests. Since user interest usually evolves
with time, it is important to understand user interest in different periods
and further model their inherent relations. To meet this end, using more
advanced sequence modeling techniques may help improve user interest modeling
in personalized news recommendation.

###  10.3. Effective and Efficient Personalized Ranking

News ranking is an essential step to make personalized news recommendations.
There are mainly three research directions to improve news ranking. First,
most existing personalized ranking methods are mainly based the coarse-grained
relevance between candidate news and user interest, which may not be optimal
for accurately targeting user interest. Although a few methods can model the
fine-grained relatedness between user and news, they are inefficient and may
not be suitable for scenarios with limited computation resources and latency
tolerance. Thus, developing both effective and efficient personalized ranking
methods is important for improving online news recommendation. Second, ranking
news solely based on relevance may lead to the filter bubble problem. It is
important to design more sophisticated news ranking strategies to achieve a
good tradeoff between accuracy and diversity. Third, most existing news
ranking methods are greedy, i.e., only consider the current ranking list in
the ranking policy. However, they may not be optimal for achieving good user
engagement in the long-term. Thus, designing proper news ranking strategies to
optimize long-term rewards may be beneficial for user experience.

###  10.4. Hyperbolic Representation Learning for News Recommendation

In most existing news recommendation methods, news and users representations
are learned in Euclidean space. Matching functions such as inner product and
cosine similarity are widely used for computing relevance scores for news
ranking. However, representation learning in Euclidean space is ineffective in
capturing the hierarchical structure of data, while hyperbolic representation
learning is much better at it. There are many inherent hierarchical data
structures in personalized news recommendation, such as different levels of
user interests, news topics, and commonsense knowledge encoded by knowledge
graphs. Thus, news recommendation with hyperbolic representation learning may
be a promising solution. There are several existing neural architectures in
hyperbolic space, such as hyperbolic attention (Gulcehre et al., 2018) and
hyperbolic GCN (Chami et al., 2019), which can serve as the core model
components in news recommendation. In addition, there have been several
successful applications of hyperbolic representation learning to CF-based
recommendation (Vinh et al., 2018; Sun et al., 2021a) and knowledge graph
embedding (Chami et al., 2020; Wang et al., 2020b), which can provide useful
guidance of collaborative signal modeling and knowledge exploitation in news
recommendation. Future research on hyperbolic representation learning may
create a new direction to overcome several drawbacks of current user/news
modeling and personalized ranking techniques conducted in Euclidean space.

###  10.5. Unified Model Training

Model training techniques are also important for learning effective and robust
personalized news recommendation models. There are four potential directions
for future works to improve model training. First, most methods only use click
signals for model training, which may be inaccurate because click signals are
usually noisy and biased. In addition, the supervision signals in specific
tasks may also be insufficient (Wu et al., 2020f). Thus, a unified framework
to incorporate various kinds of supervised and self-supervised training
signals and objectives for collaborative model learning can effectively
improve the model quality. Second, although several methods explore to use
multi-task learning frameworks to incorporate multiple objectives into model
training, they need to manually tune the loss coefficients of different tasks
in model training, which usually require much human effort and may be
sensitive to the characteristics of datasets. Thus, a self-adaptive multi-task
learning framework to automatically tune hyperparameters like loss
coefficients can reduce the developing effort and improve the model
generality. Third, many methods use randomly selected negative samples for
model training, which may be noisy and less informative. Thus, using more
effective negative sampling can help train more robust and accurate news
recommendation models. Fourth, offline trained models may have gaps with the
online scenarios and may suffer from the performance decline with time. Thus,
it is important to incorporate both offline and online learning techniques to
help the model better adapt to the latest online serving requirements.

###  10.6. News Recommendation in Social Context

On some news platforms, users may have social interactions with other users in
many ways, such as leaving comments, replies, and sharing to their social
media blogs like Twitter. The social interactions among users concerning
certain news can usually reflect their opinions, preferences, and satisfaction
on the recommended news (Wei et al., 2011), which can provide rich
complementary information to user modeling. In addition, users’ discussions
and dissemination behaviors can also help understand the content, quality and
authenticity of news (Shu et al., 2017). Besides, they can help recognize
breaking news and adjust recommendation results accordingly (Phelan et al.,
2011b). Therefore, the social contexts of news recommendation play an
important role. However, they are usually neglected by news recommendation
researches in recent years. In future researches, it is an interesting topic
to study the impacts of users’ online social interactions on the accuracy,
timeliness and quality of news personalization.

###  10.7. Privacy-preserving News Recommendation

In recent years, the ethical issues of intelligent systems have attracted much
attention from both the academia and public. Developing more responsible news
recommender systems can help better serve users of online news services with
smaller risks. One important direction for improving the responsibility of
personalized news recommendation is user privacy protection. Although a few
works like (Qi et al., 2020) explore to use federated learning techniques to
train news recommendation models in a privacy-preserving way, there are still
many challenges in developing a privacy-preserving news recommender system.
First, given a model learned in a federated way, it is still challenging to
deploy it online to serve users efficiently. Second, there may also be
potential privacy risks during the training and serving of news recommendation
models, and canonical differential privacy techniques usually lead to a heavy
sacrifice on model utility. Third, the data isolation problem in federated
learning framework settings makes it difficult to exploit some context
features like CTR and collaborative information in GNN. Thus, further
researches on developing more effective, efficient and privacy-preserving news
recommendation methods are needed.

###  10.8. Secure and Robust News Recommendation

Existing researches on news recommendation focus on building algorithms in a
trusted environment. However, in real-world scenarios there may be various
kinds of threats brought by malicious users and platforms. For example,
existing news recommendation methods are vulnerable to poisoning attacks,
which aim to promote certain items, trigger certain backdoors, or degrade the
recommender system performance. In addition, news recommender systems may be
sensitive to adversarial samples. When news recommender systems are trained in
the federated learning framework, the threats from the untrusted outside
environment become even more serious. Unfortunately, although the security and
robustness of personalized news recommender systems are critical, researches
on this problem are rather limited. Future studies on secure and robust news
recommendation are important for the stability and reliability of online news
platforms.

###  10.9. Diversity-aware News Recommendation

Besides accuracy, diversity in news recommendation also has decisive influence
on user experience. There are three main research directions to improve the
diversity of news recommendation. The first one is temporal-spatial diversity-
aware news recommendation, which aims to recommend news that are diverse from
each other and meanwhile diverse from historical clicked news. This can help
the recommendation results better satisfy users’ preference on information
variety. The second one is personalizing the diversity in news recommendation.
Different users may have different preferences on the tradeoff between
accuracy and diversity, and it may be better to consider their personalized
preference to improve user experience. The third one is fine-grained
diversity, which aims to not only diversify the content and topic of news, but
also many other factors like publishers, locations, opinions and emotions. It
has the potential to make higher-quality diversity-aware news recommendations.

###  10.10. Bias-free News Recommendation

Debiasing is another important problem in improving the responsibility of news
recommendation. The biases encoded by user behavior data will propagate to the
recommendation model and may further be amplified in the loops of
recommendation. Thus, designing effective methods to eliminate the influence
of the various kinds of biases on recommendation results is important for
making high-quality news recommendations. There are several potential research
directions in this field. First, it is important to understand the influence
of different kinds of biases on user behaviors and the recommendation model,
which can help the subsequent debiasing. Second, different users may be
influenced by the same bias information in different ways, and considering the
personalized preference of users on bias information can help better eliminate
the effects of biases. Third, there are various kinds of biases in news
recommendation. A unified debiasing framework that can simultaneously reduce
the effects of different biases can greatly improve the accuracy and
robustness of news recommendation algorithms.

###  10.11. Fairness-aware News Recommendation

Fairness is an essential but often ignored factor in personalized news
recommendation. A fair news recommender system is required to provide fair
recommendation services to different groups of users and give fair chances to
news from different providers to be recommended. Future research on fair news
recommendation can be conducted in the following three directions. First, it
is important to reduce the consumer-side unfairness related to sensitive user
attributes. Although adversarial learning techniques are mature solutions to
this problem, they are usually brittle and difficult to tune. Thus, more
robust and effective methods are required to remove the biases introduced by
sensitive user attributes. Second, different news providers and publishers are
diverse in their characteristics, such as topic preference and reputation.
Thus, it is non-trivial to properly balance the recommendation chances of news
from different providers and publishers to achieve better provider-side
fairness. Third, there are different types of fairness in the personalized
news recommendation scenario, and it is very challenging to simultaneously
achieve multi-side fairness without a heavy sacrifice of recommendation
accuracy.

###  10.12. Content Moderation in News Recommendation

The moderation of news content is important for online news platforms to avoid
recommending news with low quality or harmful content to users and mitigate
their impact on users and society. However, this issue is rarely studied and
cannot be resolved by most existing news recommendation methods. There are
three key research directions on this problem. First, it is essential to
understand the generation and spreading mechanism of harmful news as well as
their impact on users, which can help news platforms better defend toxic
content. Second, it may be useful to incorporate content moderation techniques
like fake news detection (Shu et al., 2017) and clickbait detection (Wu et
al., 2020c) into news recommendation to adjust the recommendation results
according to the quality of news content. Third, without the assistance of
additional tasks and resources, we can learn content quality-aware news
recommendation models with the guidance of certain kinds of user feedback such
as comments and dislikes, which is expected to help recommend high-quality
news to users.

###  10.13. Societal Impact of News Recommendation

News recommender systems can generate societal impact when they serve a
certain number of users. They may imperceptibly influence the opinions and
views of users when displaying personalized news content (Makhortykh and
Wijermars, 2021). Thus, it is valuable for further research to identify and
analyze the societal impact of personalized news recommendation algorithms,
such as their influence on political events, economic activities and
psychological health. In addition, research on how to reduce the potential
negative societal impact of personalized news recommendation methods can help
avoid their risky behaviors and better serve online users.

### Conclusion

Finally, we present a conclusion to this survey paper. In this survey, we
conduct a comprehensive overview of the personalized news recommendation
field, including the technologies involved in different core modules of a
personalized news recommender, the dataset and metrics for performance
evaluation, the key points for developing responsible personalized news
recommender systems, and potential directions to be explored in the future.
Different from existing survey papers that follow the conventional taxonomy of
news recommendation methods, in this paper we provide a novel perspective to
understand personalized news recommendation from its key problems and the
associated techniques and challenges. In addition, this is the first survey
paper that comprehensively covers both traditional and up-to-date deep
learning techniques for personalized news recommendation, which can provide
rich insights for extending the frontier of this field. We hope this paper can
facilitate future research on personalized news recommendation as well as
related fields in NLP and data mining.

## Acknowledgments

This work was supported by the National Natural Science Foundation of China
under Grant numbers U1936208, U1936216, 61862002, and U1705261.

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