Proposing a Hybrid Recommender System for E-commerce

The rapid growth of e-commerce has led to introducing various products from companies which are active in this area. As a result, choosing an appropriate product has become difficult for a customer. Recommender systems aimed at offering the best and most appropriate items to users based on their interests and their previous activities. This paper propose a hybrid recommender system which combines two approaches: collaborative filtering and constraint satisfaction. The proposed system, on the one hand, solves the cold start problem of the collaborative filtering technique because of using constraints. On the other hand, it solves the problem of constructing the knowledge base, which is needed for constraint-based methods. Instead of constructing the knowledge base manually, which is time consuming, the knowledge base of the constraint-based approach is extracted from interactions among the target user’s neighbors. Using the "Movielens" dataset for evaluation, the experimental results show that the performance of the proposed approach is better than the collaborative filtering method.