Recommendation with Item Clustering Based Collaborative Filtering

被引：0

作者：

Wang, Xin ^{[1
]}

Yu, Zhi ^{[1
]}

Wang, Can ^{[1
]}

机构：

[1] Zhejiang Univ, Coll Comp Sci, Zhejiang Prov Key Lab Serv Robot, Hangzhou 310027, Zhejiang, Peoples R China

来源：

PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ELECTRONIC TECHNOLOGY | 2015年 / 6卷

关键词：

recommendation; collaborative filtering; clustering;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Recommender systems are playing a more and more important roles in people's daily life and collaborative filtering (short for CF) is a widely used approach in recommender systems. In practice, many E-commerce companies such as Amazon use CF to make recommendations. However, as the number of users and items grow larger and larger, CF are suffering two kinds of problems: sparsity and scalability. So in this paper, we propose an item clustering based CF to solve these two problems. The experiments show that our method outperforms the traditional CF in term of both predicting accuracy and running time.

引用

页码：391 / 394

页数：4

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