UR: A UserBased Collaborative Filtering Recommendation System Based on Trust Mechanism and Time Weighting The technology of personalized recommendation is aimed at studying the behaviors of users, analyzing what they may be interested in and recommending suitable items to them. In other words, the personalized recommendation is to better solve the contradiction between the requirements of users and the explosive information on the Internet. The userbased collaborative filtering recommendation is one of the most successful technology for recommendation system. The most significant step of userbased collaborative filtering recommendation is comprehensive user similarity calculation. However, most recommendation systems ignore the indispensability of trust mechanism of the users and the time weighted users rating attributes in user similarity calculation, which leads to the inaccurate recommendation. Based on these issues, this paper proposes an optimized userbased collaborative filtering recommendation systemcalled UR. UR not only validates the necessity of the trust mechanism of the users and the time weighted users rating in the comprehensive user similarity calculation, but also improves the recommendation accuracy

Acquiring user information needs for recommender systems Most recommender systems attempt to use collaborative filtering, contentbased filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and contentbased filtering approach tries to recommend items that are similar to new usersu0027 profiles. The fundamental issues include how to profile new users, and how to deal with the overspecialization in contentbased recommender systems. Indeed, the terms used to describe items can be formed as a concept hierarchy. Therefore, we aim to describe user profiles or information needs by using concepts vectors. This paper presents a new method to acquire user information needs, which allows new users to describe their preferences on a concept hierarchy rather than rating items. It also develops a new ranking function to recommend items to new users based on their information needs. The proposed approach is evaluated on Amazon book datasets. The experimental results demonstrate that the proposed approach can largely improve the effectiveness of recommender systems.