管理工程学报2011，Vol.25Issue(1)：94-101,8.
电子商务协同过滤稀疏性研究:一个分类视角
Sparsity Problem in Collaborative Filtering: A Classification
摘要
Abstract
Today's e-commerce environment has drastically evolved in order to cope with information overload problems.Recommendation systems are currently used as virtual salespersons to help customers quickly locate personalized information and efficiently make purchase decisions. This technology compares the shopping behaviors and interests of users having common tendencies, and then recommends products and services for users to purchase. The more ratings on products and service the system can collect, the more accurately the system can recommend appropriate products and services to customers. However, with the ever increasing number of shoppers and products so]d, the ratings based on user-item matrix have quickly grown into becoming a higherdimensional matrix. As a result, user ratings are sparsely distributed and usually have lower than 1％. The increasing sparseness of problems has severely influenced the recommendation quality of collaborative filtering system.In Section 1, we provide an overview of the importance of user preference data. User preference data are fundamental to any ecommerce recommendation systems. The preference data include explicit ratings and implicit ratings. Explicit ratings are ratings submitted manually by users about their personal preference. Implicit ratings are ratings automatically captured and tracked by the recommendation system. The system becomes intelligent about consumer shopping behavior and produces implicit ratings over time.Data mining technologies are helping improve the precision of recommendation systems.In Section 2, we analyze six kinds of technologies with respect to their ability to potentially ameliorate sparse problems related to collaborative filtering algorithms: ( 1 ) offering default values, ( 2 ) combining content-based filtering, ( 3 ) reducing dimensionality,(4) drawing graph-theoretic approach, (5) predicting item ratings, and (6) adding user-system interactions.In Section 3, we analyze the performance of these six technologies qualitatively. The results show that dimensionality reduction is the best technology; however, its algorithms are can be hard to program. Hence, dimensionality reduction can be used as a main technology to ameliorate the sparse problem, and deserves further research and improvement. Thc other five technologies can assist the process of removing the sparse problem.This paper concludes with future research directions on the sparse problem in collaborative filtering systems. Future research directions include deeply combining collaborative filtering with content-based filtering, integrating with web log mining technologies,building efficient rating encouragement mechanisms, and sharing customer data with corporation management systems.关键词
电子商务/协同过滤/推荐算法/稀疏性Key words
E-commerce/ collaborative filtering/ recommendation algorithm/ sparse分类
管理科学引用本文复制引用
李聪,梁昌勇,杨善林..电子商务协同过滤稀疏性研究:一个分类视角[J].管理工程学报,2011,25(1):94-101,8.基金项目
四川省教育厅青年基金项目(09ZB068) （09ZB068）
