摘要： 针对协同过滤算法的数据稀疏性与冷启动问题,结合用户人口统计特征与信任机制提出一种改进推荐算法。在计算用户评分相似度时融合用户人口统计特征,产生总体相似度,通过考虑用户交互信息的局部信任和用户在整个系统中的全局信任,引入信任机制,将总体相似度和信任度相结合的混合值作为推荐权重,为用户进行推荐。实验结果表明,该算法能够有效提高冷启动用户的预测准确率。

关键词: 推荐系统, 人口统计特征, 信任机制, 协同过滤, 混合模型

Abstract: Aiming at the problem of data sparsity and cold-start in Collaborative Filtering(CF) algorithm,this paper puts forward an improved recommendation algorithm combined with user demographics and trust mechanism.When calculating the similarity of user score,it fuses the user demographics,and produces the overall similarity.In addition,it introduces the trust mechanism by considering the local trust of the users’ interaction information and global trust of user in the whole system,and puts the hybrid value that mixes the overall similarity and trust degree as the recommended weight to produce recommendations for user.Experimental results show that the proposed algorithm can effectively improve the predication accuracy of the cold start users.

Key words: recommendation system, demographics, trust mechanism, Collaborative Filtering(CF), hybrid model

中图分类号: 

• TP18