基于信任和项目偏好的协调过滤算法

Collaborative filtering algorithm based on trust and item preference

针对传统协同过滤算法不能深度挖掘用户关系,以及无法对新项目进行用户推荐的问题,提出了基于信任和用户偏好的协同过滤(TIPCF)算法。首先,通过分析用户评分判断用户的可信度并量化用户间的信任程度,挖掘用户潜在的信任关系;其次,考虑到用户之间对于不同目标项目偏好程度的差异会对用户相似性产生影响,在传统用户相似性算法上添加用户偏好度改进相似性算法;然后,通过结合用户信任度和改进的相似度,使得最近邻的选取更加准确;最后,根据用户对项目属性的偏好对新项目进行推荐。Movielens数据集实验结果表明,与传统的协同过滤算法相比,TIPCF算法的平均绝对误差减少了6.7%;在推荐新项目时,TIPCF算法的平均绝对误差减少了10.7%。TIPCF算法不仅提高了推荐的准确度,而且增加了新项目的推荐概率。

Aiming at the fact that the traditional collaborative filtering algorithm cannot deeply mine user relationship and recommend new items to users, a Trust and Item Preference Collaborative Filtering （TIPCF） recommendation algorithm was proposed. Firstly, in order to mine the latent trust relationship of the users, the user reliability was gotten and the trust degree between users was quantified by analyzing user ratings. Secondly, by considering that the difference of users＇ preference for different target items has an effect on user similarity, user preference was added to the traditional user similarity algorithm to improve the similarity algorithm. Thirdly, the choice of nearest neighbor set was more accurate by incorporating user reliability and improved similarity. Finally, the users＇ preference on item attribute was used to recommend new items. Experimental results show that, compared with traditional collaborative algorithm, the Mean Absolute Error （MAE） of TIPCF was decreased by 6.7%, and the MAE of TIPCF was decreased by 10.7% when recommending new items on the Movielens dataset. TIPCF not only improves the accuracy of recommendation, but also increases the recommended probablity of new items.