When Factorization Meets Heterogeneous Latent Topics： An Interpretable Cross-Site Recommendation Framework

Data sparsity is a well-known challenge in recommender systems. Previous studies alleviate this problem by incorporating the information within the corresponding social media site. In this paper, we solve this challenge by exploring cross-site information. Specifically, we examine： 1） how to effecti...

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Bibliographic Details
Published inJournal of computer science and technology Vol. 30; no. 4; pp. 917 - 932
Main Author 辛欣 林钦佑 魏骁驰 黄河燕
Format Journal Article
LanguageEnglish
Published New York Springer US 01.07.2015
Springer Nature B.V
Subjects
Actors
Algorithms
Artificial Intelligence
Collaboration
Computer Science
Computer simulation
Data Structures and Information Theory
Datasets
Digital media
Factorization
Information management
Information Systems Applications (incl.Internet)
Motion picture directors & producers
Performance enhancement
Ratings
Ratings & rankings
Recommendations
Recommender systems
Regular Paper
Social networks
Software Engineering
Sparsity
Studies
Theory of Computation
User behavior
Web site design
主题分析
内容特征
媒体网站
异构
推荐系统
框架
矩阵分解
站点
United States > US
topic analysis
collaborative filtering
recommender system
Online AccessGet full text
ISSN1000-9000
1860-4749
DOI10.1007/s11390-015-1570-x

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Summary:Data sparsity is a well-known challenge in recommender systems. Previous studies alleviate this problem by incorporating the information within the corresponding social media site. In this paper, we solve this challenge by exploring cross-site information. Specifically, we examine： 1） how to effectively and efficiently utilize cross-site ratings and content features to improve recommendation performance and 2） how to make the recommendation interpretable by utilizing content features. We propose a joint model of matrix factorization and latent topic analysis. Heterogeneous content features are modeled by multiple kinds of latent topics. In addition, the combination of matrix factorization and latent topics makes the recommendation result interpretable. Therefore, the above two issues are simultaneously solved. Through a real-world data.set, where user behaviors in three social media sites are collected, we demonstrate that the proposed model is effective in improving recommendation performance and interpreting the rationale of ratings.
Bibliography:Xin Xin , Chin-Yew Lin, Xiao-Chi Wei ,He-Yan Huang（1Beijing Engineering Research Center of High Volume Language Information Processing ＆ Cloud Computing, School of Computer Science, Beijing Institute of Technology, Beijing 100081, China ;2Microsoft Research Asia, Beijing 100080, China）
11-2296/TP
collaborative filtering, recommender system, topic analysis
Data sparsity is a well-known challenge in recommender systems. Previous studies alleviate this problem by incorporating the information within the corresponding social media site. In this paper, we solve this challenge by exploring cross-site information. Specifically, we examine： 1） how to effectively and efficiently utilize cross-site ratings and content features to improve recommendation performance and 2） how to make the recommendation interpretable by utilizing content features. We propose a joint model of matrix factorization and latent topic analysis. Heterogeneous content features are modeled by multiple kinds of latent topics. In addition, the combination of matrix factorization and latent topics makes the recommendation result interpretable. Therefore, the above two issues are simultaneously solved. Through a real-world data.set, where user behaviors in three social media sites are collected, we demonstrate that the proposed model is effective in improving recommendation performance and interpreting the rationale of ratings.
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ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-015-1570-x