Recommending Multiple Positive Citations for Manuscript via Content-Dependent Modeling and Multi-Positive Triplet

Abstract

Considering the rapidly increasing number of academic papers, searching for and citing appropriate references have become a nontrial task during the wiring of papers. Recommending a handful of candidate papers to a manuscript before publication could ease the burden of the authors, and help the reviewers to check the completeness of the cited resources. Conventional approaches on citation recommendation generally consider recommending one groundtruth citation for a query context from an input manuscript, but lack of consideration on co-citation recommendations. However, a piece of context often needs to be supported by two or more co-citation pairs. Here, we propose a novel scientific paper modelling for citation recommendations, namely Multi-Positive BERT Model for Citation Recommendation (MP-BERT4CR), complied with a series of Multi-Positive Triplet objectives to recommend multiple positive citations for a query context. The proposed approach has the following advantages: First, the proposed multi-positive objectives are effective to recommend multiple positive candidates. Second, we adopt noise distributions which are built based on the historical co-citation frequencies, so that MP-BERT4CR is not only effective on recommending high-frequent co-citation pairs; but also the performances on retrieving the low-frequent ones are significantly improved. Third, we propose a dynamic context sampling strategy which captures the "macro-scoped" citing intents from a manuscript and empowers the citation embeddings to be content-dependent, which allow the algorithm to further improve the performances. Single and multiple positive recommendation experiments testified that MP-BERT4CR delivered significant improvements. In addition, MP-BERT4CR are also effective in retrieving the full list of cocitations, and historically low-frequent co-citation pairs compared with the prior works. CCS CONCEPTS• Information systems → Recommender systems; Content analysis and feature selection.