Learning Domain Semantics and Cross-Domain Correlations for Paper Recommendation

Abstract

Understanding how knowledge is technically transferred across academic disciplines is very relevant for understanding and facilitating innovation. There are two challenges for this purpose, namely the semantic ambiguity and the asymmetric influence across disciplines. In this paper we investigate knowledge propagation and characterize semantic correlations for cross discipline paper recommendation. We adopt a generative model to represent a paper content as the probabilistic association with an existing hierarchically classified discipline to reduce the ambiguity of word semantics. The semantic correlation across disciplines is represented by an influence function, a correlation metric and a ranking mechanism. Then a user interest is represented as a probabilistic distribution over the target domain semantics and the correlated papers are recommended. Experimental results on real datasets show the effectiveness of our methods. We also discuss the intrinsic factors of results in an interpretable way. Compared with traditional word embedding based methods, our approach supports the evolution of domain semantics that accordingly lead to the update of semantic correlation. Another advantage of our approach is its flexibility and uniformity in supporting user interest specifications by either a list of papers or a query of key words, which is suited for practical scenarios.