PP -Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity (2021)

Abstract: 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. However, these methods usually have difficulties in making accurate recommendations to cold-start users, and tend to recommend similar news with those users have read. In general, popular news usually contain important information and can attract users with different interests. Besides, they are usually diverse in content and topic. Thus, in this paper we propose to incorporate news popularity information to alleviate the cold-start and diversity problems for personalized news recommendation. In our method, the ranking score for recommending a candidate news to a target user is the combination of a personalized matching score and a news popularity score. The former is used to capture the personalized user interest in news. The latter is used to measure time-aware popularity of candidate news, which is predicted based on news content, recency, and real-time CTR using a unified framework. Besides, we propose a popularity-aware user encoder to eliminate the popularity bias in user behaviors for accurate interest modeling. Experiments on two real-world datasets show our method can effectively improve the accuracy and diversity for news recommendation.

## introduction personalized news recommendation is a useful technique to help users alleviate information overload when visiting online news platforms @xcite @xcite . existing personalized news recommendation methods usually recommend news to a target user based on the matching between the content of candidate news and user interest inferred from previous behaviors @xcite . for example, wu et al. the motivation for this work is that popular news usually convey important information such as catastrophes, epidemics, presidential election and so on, as shown in fig. 1 . these popular news can attract many users to read and discuss even if they have different personal interest @xcite . in addition, popular news are diverse in content and can cover many different topics @xcite . thus, incorporating popular news has the potential to alleviate the cold-start and diversity problems in personalized news recommendation. in this paper, we propose a new method named pp-rec for news recommendation foot_0 , which can consider not only personalized user interest in news but also the popularity of candidate news. in our method, the ranking score of recommending a candidate news to a target user is the combination of a personalized matching score and a news popularity score. the personalized matching score is used to measure personal user interest in the content of candidate news. the news popularity score is used to measure the time-aware popularity of candidate news. since news popularity is influenced by many different factors such as content and freshness, we propose a unified model to predict time-aware news popularity based on news content, recency, and near real-time click-through rate (ctr). these two scores are combined via a personalized aggregator for news ranking, which can capture the personalized preferences of different users in popular news. moreover, we propose a knowledge-aware news encoder to generate news content embeddings from both news texts and entities. besides, since news popularity can effect users' click behaviors @xcite and lead to bias in behavior based user interest modeling, we propose a popularity-aware user encoder which can consider the popularity bias in user behaviors and learn more accurate user interest representation. extensive experiments on two real-world datasets show pp-rec can effectively improve the performance of news recommendation in terms of both accuracy and diversity. ## methodology in this section, we introduce pp-rec for news recommendation which can consider both the personal interest of users and the popularity of candidate news. first, we introduce the overall framework of pp-rec, as shown in fig. 2 . then we introduce the details of each module in pp-rec, which are shown in figs. 3 , 4 and 5 . ## conclusion in this paper, we propose a new news recommendation method named pp-rec to alleviate the coldstart and diversity problems of personalized news recommendation, which can consider both the personal interest of users and the popularity of candidate news. in our method, we rank the candidate news based on the combination of a personalized matching score and a news popularity score. we propose a unified model to predict time-aware news popularity based on news content, recency, and near real-time ctr. in addition, we propose a knowledge-aware news encoder to generate news content embeddings from news texts and entities, and a popularity-aware user encoder to generate user interest embeddings from the content and popularity of clicked news. extensive experiments on two real-world datasets constructed by logs of commercial news websites and feeds in microsoft validate that our method can effectively improve the accuracy and diversity of news recommendation.