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

摘要： 推荐系统作为一种程序算法，是通过度量用户对给定商品的的喜好程度做个性化推荐。广泛地说，推荐系统试图总结出用户的个人喜好，并在用户和商品之间建立一种关系模型。与其他奇异值分解方法相比，改进的增量奇异值分解协同过滤算法基于一系列评分值对用户-商品矩阵进行分解，每次产生一对当前最重要的特征向量。算法有着最小的内存需求，扩展性高，特别适合处理大规模数据集；算法的有效性在Netflix数据集上得到了验证。

关键词: 奇异值分解, 推荐系统, 协同过滤