E-commerce Environment, Collaborative Filtering Recommendation Method Of Analysis And Research,

With the popularization of Internet and the rapid development of E-commerce, many famous E-Commerce sites have developed Recommender system for providing personalization service to consumers. Recommender systems are used by E-commerce sites to suggest products their customers and to provide consumers with information to help them decide which products to purchase. On the aspect of theory research and factual application, E-commerce recommender systems have developed rapidly. Recommendation approaches are in the heart of recommender system, so which approach is adopted is crucial to the success of recommending quality as well as efficiency. Today, five approaches are used by recommender systems to generate recommendations, namely knowledge engineering, collaborative filtering, content-based, hybrid and data mine approaches. Collaborative filtering has been very successful in both research and practice. But, with expansion of E-commerce system's size , collaborative filtering approach suffer from many challenges, for instance, quality of recommendations, scalability, sparsity, cold-start problem. We studied deeply E-commerce recommender system and analysed challenges which collaborative filtering recommendation approach suffered from, then we presented an improved collaborative filtering algorithm. The main idea of improved algorithm is as follows: First, to compute offline items similarity, and to save results in database; Second, in the step of user's neighbors formation, we not only consider correlation of users, but also use precomputed items similiarity to predicted ratings of items which were not rated; Finally, to predicte every item's rating according to the nearest neighbors and to generate recommendation to target user. We designed experiment test system and tested this improved algorithm. The experiment result proved that this algorithm is logical and effective. Compare to tranditional collaborative filtering algorithm, this algorithm can overcome the sparsity of user's rating information and generate better recommdation results.