关键词: 奇异值分解, 推荐系统, 协同过滤

Abstract:

As a kind of programs and algorithms，recommendation systems provide personalized recommendations by measuring
the preference levels of users（customers） on the given commodities.More broadly，recommender systems attempt to profile user preferences and model the interaction between users and products.Compared with other singular value decomposition methods，the improved incremental singular value decompositon allows the singular value decomposition of a user-item rating matrix to be learned based on single observation presented serially，and produces singular vector pairs one at a time，each time forms the most significant one at present.The algorithm has minimal memory requirements，high scalability and is particularly suitable for handling large data sets.The technique is demonstrated on the Netflix dataset.

Key words: singular value decomposition, recommender system, collaborative filtering