S enti R ec: Sentiment Diversity-aware Neural News Recommendation (2020)

Abstract: Personalized news recommendation is important for online news services. Many news recommendation methods recommend news based on their relevance to users’ historical browsed news, and the recommended news usually have similar sentiment with browsed news. However, if browsed news is dominated by certain kinds of sentiment, the model may intensively recommend news with the same sentiment orientation, making it difficult for users to receive diverse opinions and news events. In this paper, we propose a sentiment diversity-aware neural news recommendation approach, which can recommend news with more diverse sentiment. In our approach, we propose a sentiment-aware news encoder, which is jointly trained with an auxiliary sentiment prediction task, to learn sentiment-aware news representations. We learn user representations from browsed news representations, and compute click scores based on user and candidate news representations. In addition, we propose a sentiment diversity regularization method to penalize the model by combining the overall sentiment orientation of browsed news as well as the click and sentiment scores of candidate news. Extensive experiments on real-world dataset show that our approach can effectively improve the sentiment diversity in news recommendation without performance sacrifice.

## introduction online news websites such as google news 1 have gained huge popularity for consuming digital news @xcite . however, it is difficult for users to find their interested news information due to the huge volume of news emerging every day @xcite . thus, personalized news recommendation is important for news websites to 1 https://news.google.com/ driving the highway 1 north of san francisco 'snack man' helps feed the homeless 3 dead, 3 injured in plane crash west of michigan airport 1 killed in shooting at washington state apartment building 4 dead in horrific wrong-way crash on highway 101 in san francisco clicked news candidate news recommend … … early morning christmas fire displaces 250 at shelter for homeless target user interest and alleviate information overload @xcite . many existing news recommendation methods rank candidate news based on their relevance to the interests of users inferred from their historical browsed news @xcite . for example, @xcite proposed to learn news representations from news texts via autoencoders, and learn user representations from browsed news using a gated recurrent unit (gru) network. they ranked candidate news based on the inner product of the user representation and candidate news representation. @xcite proposed to learn news and user representations using multi-head self-attention networks. they ranked news based on the click scores computed by the dot product between news and user representations. the news articles recommended by these methods are usually similar to those previously browsed by a user in many aspects, such as content and sentiment. for example, in fig. 1 the two candidate news articles are recommended to both users. the first user browses a news about the highway in san francisco and a news about a person helping the homeless, which has inherent relatedness with the content of the candidate news. the second user browses several news about deadly accidents and crime, which has the same sentiment orientation as the candidate news. however, like the recommendations for the second user in fig. 1 , if a user mainly browses news articles that have a certain kind of sentiment (e.g., negative sentiment), many existing methods may intensively recommend news with the same sentiment orientation, which is not beneficial for this user to receive diverse opinions and news events that convey other sentiments. in this paper, we propose a sentiment diversityaware news recommendation approach named sen-tirec, which can improve the sentiment diversity of news recommendation by considering the sentiment orientation of candidate and browsed news. in our approach, we propose a sentiment-aware news encoder, which is jointly trained with an auxiliary news sentiment prediction task, to incorporate sentiment information into news modeling and generate sentiment-aware news representations. we learn user representations from the representations of browsed news, and compute the click scores of candidate news based on their relevance to the user representations. in addition, to enhance the sentiment diversity of news recommendation, we propose a sentiment diversity regularization method to penalize our model during model training, which is based on the overall sentiment orientation of browsed news as well as the sentiment scores and click scores of candidate news. we conduct extensive experiments on a real-world benchmark dataset, and the results show that our approach can achieve better sentiment diversity and recommendation accuracy than many baseline methods. the contributions of this paper are summarized as follows: ## related work news recommendation is an important technique for online news websites to provide personalized news reading services @xcite . a core problem in news recommendation is building accurate representations of news and users and further ranking candidate news according to news and user representations @xcite . in many news recommendation methods, news ranking is based on the representations of news and users built by manual feature engineering @xcite @xcite @xcite @xcite @xcite . for example, @xcite proposed to use topic categories and interest features generated by a bayesian model to build news and user representations. they ranked candidate news based on the product of a content-based score computed from news representations and a filter-based score computed by collaborative filtering. @xcite proposed an explicit localized semantic analysis (elsa) model for location-based news recommendation. they proposed to represent news and users by extracting topic and location features from wikipedia pages, and ranked news based on the cosine distance between the representations of news and user. @xcite proposed to use various handcrafted features to represent news and users, such as title length, news categories, user profiles and features extracted from user behavior histories. they ranked candidate news based on the click scores computed by a neural factorization machine. however, these methods rely on manual feature engineering to build news and user representations, which usually necessitate massive expertise. in addition, handcrafted features may not be optimal in representing news content and user interest. in recent years, several news recommendation methods based on deep learning techniques are proposed @xcite @xcite @xcite @xcite . for example, @xcite proposed to learn first news representations from news bodies using autoencoders, and then learn representations of users from their clicked news with a gru network. candidate news are ranked based on the click scores computed by the dot products between news and user representations. @xcite proposed to learn news representations from news titles and their entities via a knowledge-aware cnn network, and learn user representations from clicked news with a candidate-aware attention network. they ranked candidate news based on the click scores computed from the concatenation of news and user representations via a feed-forward neural network. @xcite proposed to learn news and user representations with a combination of multi-head self-attention and additive attention networks. they also used dot product to compute click scores for news ranking. these methods tend to recommend news articles which are similar with the news users previously browsed @xcite . thus, these methods may recommend news with similar sentiment orientation with those previously browsed by users, which is not beneficial for users to receive diverse news information. different from these methods, our approach can effectively recommend news with diverse sentiment to users by incorporating sentiment information into news modeling via a sentiment-aware news encoder and regularizing the model based on the sentiment orientation of browsed and candidate news. ## our approach in this section, we first present the formal definitions of the problem explored in this paper, then introduce the details of our sentiment diversity-aware news recommendation (sentirec) approach. ## conclusion and future work in this paper, we propose a sentiment diversityaware neural news recommendation approach which can effectively recommend news with diverse sentiment from browsed news. we propose a sentiment-aware news encoder to learn sentimentaware news representations by jointly training it with an auxiliary sentiment prediction task. we learn user representations from representations of browsed news, and compute click scores based on user and candidate news representations. in addition, we propose a sentiment diversity regularization method to regularize the model according to the overall sentiment orientation of browsed news as well as the click scores and sentiment scores of candidate news. extensive experiments on realworld benchmark dataset validate that our approach can effectively enhance the sentiment diversity of news recommendation without hurting the recommendation performance. in our future work, we plan to analyze the sentiment on the entities in news and explore to improve the entity-level sentiment diversity of news recommendation. in addition, we plan to extend sentiment polarities to more kinds of emotions, such as angry, happiness, sad and surprise, to enhance the emotion diversity of news recommendation.