Collaboratively Learning Latent Factors and Correlations for New Paper Influence Predication

There are an increasing number of papers published every year. It is desired for researchers to find the new high-quality papers, which is also a challenging task due to the lack of citation information. In this paper, we propose a novel method to predicate a new paper influence by collaboratively learning the latent vectors of paper features and correlations. We propose the concept topic related authority to integrate the dynamic topic model with paper citations so as to learn how content and authors influence a paper quality. We adopt the Factorization Machine method to collaboratively learn the latent vectors of correlations between different paper features. Comparing with traditional methods, it does not require the citation information to evaluate a paper quality, which is appropriate for new published papers. We conduct extensive evaluation against a real dataset crawled from ACM Digital Library. The results show that our method outperforms the other methods.