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

页码：245 / 252

页数：8

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