Personalized news recommendation based on articles chain building

News personalized recommendation has long been a favorite research in recommender. Previous methods strive to satisfy the users by constructing the users’ preference profiles. Traditionally, most of recent researches use users’ reading history (content based) or access pattern (collaborative filtering based) to recommend newly published news to them. In this way, they only considered the relationship between news articles and the users and ignored the context of news report background. In other words, they fail to provide more useful information with considering the progression of the news story chain. In this paper, we propose to define the quality of a news story chain. Besides, we propose a method to construct a news story chain on a news corpus with date information. At last, we use a greedy selection method for filtering the final recommended news articles with considering accuracy and diversity. In this way, we can provide the news articles for users and meet their requirement: after reading the recommended news, the user gains a better understanding of the progression of the news story they read before. Finally, we designed several experiments compared to the state-of-the-art approaches, and the experimental results show that our proposed method significantly improves the accuracy, diversity and NDCG metrics.