An Improved Collaborative Filtering Recommendation Algorithm Based on Case-Based Reasoning

Collaborative filtering recommendation is a popular recommendation algorithm in electronic commerce, but the disadvantage of cold start still exists in the reason of new coming users and items. CBR (case-based reasoning) is used to get source case in history according to the notation of target case, and the source case plays a guiding role in solving the problem of the target case. It is a useful algorithm to evaluate the solution of target case, and explain the abnormal phenomenon of target case. In this paper we applied case-based reason with forgetting mechanism to solve the cold start problem in collaborative filtering, to deduce the score of items which is not scored by user, and then to recommend items with TOP-N collaborative filtering. Experimental results show that the proposed collaborative filtering combining case-based reasoning could significantly ease cold start problem.