A High Performance Content Based Recommender System Using Hypernym Expansion

Abstract

There are two major limitations in content-based recommender systems, the first is accurately measuring the similarity of preferred documents to a large set of general documents, and the second is over-specialization which limits the "interesting" documents recommended from a general document set. To address these issues, we propose combining linguistic methods and term frequency methods to improve overall performance and recommendation.