Researchers spend considerable time searching for relevant papers on the topic in which they are currently interested. Often, despite having similar interests, researchers in the same lab do not find it convenient to share results of bibliographic searches and thus conduct independent time-consuming searches. Research paper recommender systems can help the researcher avoid such time-consuming searches by allowing each researcher to automatically take advantage of previous searches performed by others in the lab. Existing recommender systems were developed for commercial domains to assist users by focusing towards products of their interests. Unlike those domains, the research paper domain has relatively few users when compared with the huge number of research papers. In this paper we present a novel system to recommend relevant research papers to a user based on the user's recent querying and browsing habits. The core of the system is a scalable subspace clustering algorithm, SCuBA (Subspace ClUstering Based Analysis) that performs well on the sparse, high-dimensional data collected in this domain. Both synthetic and benchmark datasets are used to evaluate the recommendation system and to demonstrate that it performs better than the traditional collaborative filtering approaches when recommending research papers.
ASJC Scopus subject areas
- General Computer Science