Research on personalized recommendation system for e-commerce based on web log mining and user browsing behaviors

Web server log files and customers transaction data can be mined meaningful user access patterns to anticipate potential customers so as to enable personalized information services and targeted e-commerce activities. The paper bases on Clustering technology of Web Mining to provide a personalized solution to implement an e-commerce recommendation system. The paper introduces the UserID-URL associated matrix according to log information, We calculate UserID-URL associated matrix and Distance matrix to cluster users into user groups. Clustering algorithm is simple and easy to achieve due to improve the nature of algorithm, no such the candidate set of Apriori algorithm in association rules. The system can recommend the goods which other users of this cluster browse to the user and achieve the objective of personalized goods recommendation.