Collaborative topic modeling for recommending scientific articles

TLDR

Large online archives make finding relevant papers difficult, but new online communities that share citations offer a promising solution. The study develops an algorithm to recommend scientific articles to users of such online communities. The algorithm merges collaborative filtering with probabilistic topic modeling to create an interpretable latent structure for users and items, enabling recommendations for both existing and newly published articles. On a large CiteULike dataset, the algorithm outperforms traditional collaborative filtering, delivering more effective recommendations.

Abstract

Researchers have access to large online archives of scientific articles. As a consequence, finding relevant papers has become more difficult. Newly formed online communities of researchers sharing citations provides a new way to solve this problem. In this paper, we develop an algorithm to recommend scientific articles to users of an online community. Our approach combines the merits of traditional collaborative filtering and probabilistic topic modeling. It provides an interpretable latent structure for users and items, and can form recommendations about both existing and newly published articles. We study a large subset of data from CiteULike, a bibliography sharing service, and show that our algorithm provides a more effective recommender system than traditional collaborative filtering.