Hybrid parallel approach for personalized literature recommendation system

Researchers regularly access and review large amounts of literatures. In the previous work, we presented a bookmarklet-triggered literature sharing system, which combines bibliography functionalities along with DOI content negotiation services. In this paper, we have made secondary development work to integrate literature recommendation functionalities into this system. We introduce a hybrid approach in parallel to recommend related articles to researchers. First, we collect a large amount of published and new articles using crawlers and RSS listeners to address cold start issue. Second, we adopt Latent Dirichlet Allocation (LDA) as the topic model to category literatures. For one kind of literatures related to researchers' interest, we use collaborative filtering techniques to make further analysis based on implicit user feedbacks in this system. Finally, we take matrix factorization with Alternating Least Squares (ALS) in parallel to compute the top-N recommendations per user.