Collaboration filtering recommendation optimization with user implicit feedback

被引：0

作者：

Cui, Haomin ^{[1
]}

Zhu, Ming ^{[1
]}

机构：

[1] Joint Laboratory of Network Communication System & Control Key Lab of Anhui, Department of Automation, University of Science and Technology of China, Hefei

来源：

Journal of Computational Information Systems | 2014年 / 10卷 / 14期

关键词：

Collaboration filtering; Implicit feedback; Recommender system;

D O I：

10.12733/jcis10758

中图分类号：

学科分类号：

摘要：

Collaboration Filter is one of well-known effective methods for recommendation. The suggestions are based on the mass of user ratings for various items, which are used as explicit feedback. However, implicit feedback such as the time spent on website, how soon the user skipped the song, and the sequence of selected items have also proved to be useful in recommender systems. In this paper, we proposed a hybrid recommendation algorithm considering both explicit and implicit feedback to produce better recommended list. An iteration process is proposed for using implicit feedback in order to find effective feedback for recommendation. Time window is also used to limit the impact range of implicit feedback. Experimental results on MovieLens datasets show that the implicit feedback can effectively compensate for the shortcomings of explicit feedback and the proposed algorithm is more accurate than the traditional one. © 2014 by Binary Information Press

引用

页码：5855 / 5862

页数：7

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