Addressing Cold Start Problem in Collaborative Filtering using Demographic Data with Entropy-based Methodology

Abstract

Collaborative filtering based recommender systems (RS) are faced with cold start problem. This problem arises when the RS does not have enough information or opinion about a person or about a product and therefore cannot make recommendation for such person. In this paper, the demographic data of the user such as age, gender, and occupation are utilized as additional sources together with existing users’ rating to tackle the cold start problem by employing the entropy-based methodology to determine the degree of predictability. Experimental results on MovieLens dataset showed that the proposed method gives higher accuracy than other existing demographic based methods. Keywords— Cold Start, Collaborative Filtering, Entropy, Demographic Approach, Recommender Systems