关键词: 推荐系统, 最近邻, 用户相似性, 维数简化

Abstract: Collaborative filtering is the most successful and widely used recommendation technology in E-commerce recommender systems. However, traditional collaborative filtering algorithm faces severe challenge of sparse user ratings and real-time recommendation. Aiming at the problem of data sparsity for collaborative filtering, a high efficient personalization recommendation algorithm based on nearest neighbor is proposed. The algorithm refines the user ratings data using dimensionality reduction, uses a new similarity measure to find the target users’neighbors, and generates recommendations. The experimental results argue that the algorithm efficiently improves sparsity of rating data, and provides better recommendation results than traditional collaborative filtering algorithms.

Key words: recommendation system, nearest neighbor, user similarity, dimensionality reduction