Subject-relevant Document Recommendation: A Reference Topic-Based Approach

Knowledge-intensive workers, such as academic researchers, medical professionals or patent engineers, have a demanding need of searching information relevant to their work. Content-based recommender system (CBRS) makes recommendation by analyzing similarity of textual contents between documents and users’ preferences. Although content-based filtering has been one of the promising approaches to document recommendations, it encounters the over-specialization problem. CBRS tends to recommend documents that are similar to what have been in user’s preference profile. Rationally, citations in an article represent the intellectual/affective balance of the individual interpretation in time and domain understanding. A cited article shall be associated with and may reflect the subject domain of its citing articles. Our study addresses the over-specialization problem to support the information needs of researchers. We propose a Reference Topic-based Document Recommendation (RTDR) technique, which exploits the citation information of a focal user’s preferred documents and thereby recommends documents that are relevant to the subject domain of his or her preference. Our primary evaluation results suggest the outperformance of the proposed RTDR to the benchmarks

Content-based Recommendation Services for Personalized Digital Libraries

This paper describes the possible use of advanced content-based recommendation methods in the area of Digital Libraries. Content-based recommenders analyze documents previously rated by a target user, and build a profile exploited to recommend new interesting documents. One of the main limitations of traditional keyword-based approaches is that they are unable to capture the semantics of the user interests, due to the natural language ambiguity. We developed a semantic recommender system, called ITem Recommender, able to disambiguate documents before using them to learn the user profile. The Conference Participant Advisor service relies on the profiles learned by ITem Recommender to build a personalized conference program, in which relevant talks are highlighted according to the participant's interests