A Cluster Based Collaborative Filtering Method for Improving the Performance of Recommender Systems in E-Commerce

摘要

Rapid growth of E-commerce has made a huge number of products and services accessible to the users. The vast variety of options makes it difficult for the users to finalize their decisions. Recommender systems aim at offering the most suitable items to the users. In this paper, a collaborative filtering recommender system, called CFGA, is proposed which consists of two phases: offline and online. In the offline phase, users are clustered based on their similarities using genetic algorithm; and in the online phase, items which are interesting for a user's cluster members are recommended to that user. CFGA is evaluated with Movielens dataset and experimental results show that CFGA outperforms several existing recommendation methods in terms of accuracy.