Recommender Systems to Address New User Cold-Start Problem with User Side Information
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2016, Vol 18, Issue 2
Abstract
Abstract: Due to exponential growth of Internet, users are facing the problem of Information overloading. Recommender Systems (RS) serve as an indispensible tool to solve information overloading problem. Due totheir great commercial value, recommender systems have also been successfully deployed in industry, such as product recommendation at Amazon, music recommendation at iTunes, movie recommendation at Netflix, etc. Collaborative Filtering (CF) is one of the popular approaches for Recommender systems based on user-item rating matrix. The main challenges faced by CF are Sparsity, Cold-Start and Scalability. In this paper we proposed a new approach to use user’s side information in addition to user-item rating matrix to address new user cold-start problem. User’s side information is obtained from Social Networks, which are the most activesource of information now a day. First CF method is used to form user clusters based on the similarity of users. User’s side information is obtained from Social networks and the same is used to form a Social matrix. Finallycombine user history with social matrix to provide recommendations to new users. The experimental results proved that the performance of RS is improved over traditional CF systems.
Authors and Affiliations
M. Sunitha , Dr. T. Adilakshmi
Keywords
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