Item-Based Collaborative Filtering with Attribute Correlation: A Case Study on Movie Recommendation

被引：0
|
作者：
Pirasteh, Parivash [1 ]
Jung, Jason J. [1 ]
Hwang, Dosam [1 ]
机构：
[1] Yeungnam Univ, Dept Comp Engn, Gyongsan, Gyeongsangbuk D, South Korea
来源：
INTELLIGENT INFORMATION AND DATABASE SYSTEMS, PT II | 2014年 / 8398卷
关键词：
Recommender systems; Item-based collaborative filtering; Attribute correlation; SPARSITY PROBLEM;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
User-based collaborative filtering (CF) is a widely used technique to generate recommendations. Lacking sufficient ratings will prevent CF from modeling user preference effectively and finding trustworthy similar users. To alleviate this problems, item-based CF was introduced. However, when number of co-rated items is not enough or new item is added to the system, item-based CF result is not reliable, too. This paper presents a new method based on movies similarity that focuses on improving recommendation performance when dataset is sparse. In this way, we express a new method to measure the similarity between items by utilizing the genre and director of movies. Experiments show the superiority of the measure in cold start condition.
引用
收藏
页码：245 / 252
页数：8
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