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Nowadays, news apps have taken over the popularity of paper-based media,providing a great opportunity for personalization. Recurrent Neural Network(RNN)-based sequential recommendation is a popular approach that utilizesusers' recent browsing history to predict future items. This approach islimited that it does not consider the societal influences of news consumption,i.e., users may follow popular topics that are constantly changing, whilecertain hot topics might be spreading only among specific groups of people.Such societal impact is difficult to predict given only users' own readinghistories. On the other hand, the traditional User-based CollaborativeFiltering (UserCF) makes recommendations based on the interests of the"neighbors", which provides the possibility to supplement the weaknesses ofRNN-based methods. However, conventional UserCF only uses a single similaritymetric to model the relationships between users, which is too coarse-grainedand thus limits the performance. In this paper, we propose a framework of deepneural networks to integrate the RNN-based sequential recommendations and thekey ideas from UserCF, to develop Collaborative Sequential RecommendationNetworks (CSRNs). Firstly, we build a directed co-reading network of users, tocapture the fine-grained topic-specific similarities between users in a vectorspace. Then, the CSRN model encodes users with RNNs, and learns to attend toneighbors and summarize what news they are reading at the moment. Finally, newsarticles are recommended according to both the user's own state and thesummarized state of the neighbors. Experiments on two public datasets show thatthe proposed model outperforms the state-of-the-art approaches significantly.

CSRN: Collaborative Sequential Recommendation Networks for News Retrieval