An Adapted Alternation Approach for Recommender Systems

摘要：

This paper presents an adaptation of the Alternation technique to tackle the prediction task in recommender systems. These systems are widely considered in Electronic commerce to help customers to find products they will probably like or dislike. As the SVD-based approaches, the proposed adapted Alternation technique uses all the information stored in the system to find the predictions. The main advantage of this technique with respect to the SVD-based ones is that it can deal with missing data. Furthermore, it has a smaller computational cost. Experimental results with public data sets are provided in order to show the viability of the proposed adapted Alternation approach.

作者: Carme Julià Angel D.Sappa Felipe Lumbreras Joan Serra Antonio Lòpez

作者单位: Computer Vision Center, Edifici O, Campus UAB, 08193 Bellaterra, SPAIN Computer Science Department, Universitat Autònoma de Barcelona Computer Vision Center Edifici O, Cam tComputer Science Department, Universitat Autònoma de Barcelona Computer Vision Center Edifici O, Ca

会议类型: 国际会议

会议名称: AiR08，EM2108，SOAIC08，SIOKM08，BIMA08，DKEEE08(2008IEEE国际电子商务工程学术会议)

会议地点: 西安

会议语种:英文

页码: 128-135

在线出版日期: 2008-10-22（万方平台首次上网日期，不代表论文的发表时间）