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群体推荐是个性化推荐领域一个新的研究热点,其与传统个体推荐的重要区别之一,是需要群体成员进行协商或谈判,以提高群体满意度。针对该问题,提出了基于案例推理和协商的群体推荐算法,根据群体成员对项目的历史评价作为知识库,从群体用户角度出发进行协商或谈判,使用多Agent系统模拟群体用户在选择推荐项目问题上的协商过程,最终达成一致并进行推荐,推荐完成后通过用户反馈对群体成员的知识库进行及时更新。选取MovieLens数据库进行试验评价,结果表明,文中算法的推荐质量明显优于对比算法。
Group recommendation is a new research hotspot in the field of personalized recommendation. One of the important differences between the recommendation and the traditional individual recommendation is the need for group members to negotiate or negotiate to enhance the group satisfaction. In view of this problem, this paper proposes a group recommendation algorithm based on case-based reasoning and negotiation. According to the historical evaluation of group members as a knowledge base, the group users negotiate or negotiate from the perspective of group users, and use the multi-agent system to simulate group users’ On the process of consultation, and finally reached an agreement and recommended, the recommendation is completed through user feedback on the members of the group’s knowledge base for timely updates. The MovieLens database is selected for experimental evaluation. The results show that the recommended quality of the proposed algorithm is obviously better than that of the comparative algorithm.