关键词: 协同过滤推荐系统, 类搜索方法, 倒排索引

Abstract: Nearest neighbor computation is a typical collaborative filtering approach for high recommendation accuracy. However, this approach is not scalable given the huge number of customers and items in typical commercial applications, cluster-based collaborative filtering techniques can be a remedy for the problem, but they usually provide relatively lower accuracy. This paper provides an efficient implementation of cluster search strategy by adapting a specifically tailored cluster-skipping inverted index structure. Experimental results reveal that this is a good solution for high accuracy and reasonable scalability of the recommender system.

Key words: collaborative filtering recommendation system, clustering search method, inverted index