In the E-commerce, the recommendation is aimed at suggesting products to customers and providing consumers with information to help them decide which products to purchase. There are mainly three kinds of methods in a recommendation system in the previous literatures：Contents-based, Collaborative-filtering and Hybrid recommendation. Collaborative filtering is the most successful recommender system in both research and applications such as information filtering and E-commerce. However, it is still has restriction in recommending new items. In this paper, we focus on only Collaborative-filtering recommendation and try to remedy its restriction of implementations. We introduce a novel expansion approach, called the attribute-based mechanism that is based on the architecture of traditional collaborative-filtering recommendation and connected with the technique of attribute extraction. Our approach considers the purpose of recommendation not only as the promotion of existing items, but also as looking for the potential preference of users, who are expected to promote new items. Three main contributions can be presented：first, we offer other perspective to think about the nature of recommending system. Differing from the traditional CF approach emphasizing the prediction of the behavior of new consumers, this mechanism stresses the maintenance of the satisfaction of existing customers. Besides, we eliminate the recommendation limitation of the traditional CF approach through considering the purchasing motive of customers. Second, the attribute-based mechanism combines aspects of collaborative filtering and attribute extraction to recommend new items for a user based on their prior purchase behavior. Analysis results obtained during the experiments have shown that most users’ evaluations are higher than the mean. Third, we provide comparative results on the impact of parameters like the number of recommending items, the value of attribute threshold, etc.
