SentiRec: Sentiment Diversity-aware Neural News Recommendation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
DiversityNews RecommendationSimilar Papers 제목 키워드 기반
End-to-end Learnable Diversity-aware News Recommendation
Diversity is an important factor in providing high-quality personalized news recommendations. However, most existing news recommendation methods only aim to optimize recommendation accuracy while ignoring diversity. Rera…
DiversityNews RecommendationRerankingIs News Recommendation a Sequential Recommendation Task?
News recommendation is often modeled as a sequential recommendation task, which assumes that there are rich short-term dependencies over historical clicked news. However, in news recommendation scenarios users usually ha…
DiversityNews RecommendationSequential RecommendationRADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations
In traditional recommender system literature, diversity is often seen as the opposite of similarity, and typically defined as the distance between identified topics, categories or word models. However, this is not expres…
DiversityRecommendation SystemsPP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity
Personalized news recommendation methods are widely used in online news services. These methods usually recommend news based on the matching between news content and user interest inferred from historical behaviors. Howe…
DiversityNews RecommendationDKN: Deep Knowledge-Aware Network for News Recommendation
Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense.…
Click-Through Rate PredictionCommon Sense ReasoningNews RecommendationRecommendation Systems