摘要
针对传统推荐系统存在的用户评分稀疏性和系统扩展性问题,提出了一种基于智能多agent的推荐系统MASRS。首先采用余弦公式处理用户-项评分矩阵得到用户初始邻居集;然后将用户评分映射到相应项的属性值上,生成用户-属性值偏好矩阵UPm,并基于此矩阵进行用户相似性度量,得到用户产品推荐集,该方法有效缓解用户评分稀疏性问题;通过智能多agent架构推荐系统,使大量复杂计算在线下进行,从而改善系统存在的扩展性问题。同时实验表明新系统在推荐精度上收敛性更好。
Traditional recommendation system has the problem of sparse user ratings and system scalability. This paper proposes a recommendation system based on intelligence multi-agentl At first, the cosine similarity measure has been used to handle user-item rating matrix, thus the initial neighbor set for target users can be gained. Then, user ratings have been mapped to relevant item attributes for generating user-attributes value preference matrix UPm of each user. Thus, user similarity can be computed based on UPm and rating sparsity has been alleviated simultaneously. The recommendation system of intelligence multi-agent makes calculating an online processing, and thus improves the system scalability. Experimental results show that the new system achieves a betteraccuracy in recommended convergence.
出处
《计算机系统应用》
2010年第2期1-5,共5页
Computer Systems & Applications
关键词
推荐系统
稀疏性
用户-属性值偏好矩阵
智能多agent
recommendation system
sparsity
user-attributes value preference matrix
intelligence multi-agent
