Integrating heterogeneous information via flexible regularization framework for recommendation
文献类型：期刊论文
| 作者 | Shi, Chuan2; Liu, Jian2; Zhuang, Fuzhen3; Yu, Philip S.1; Wu, Bin2 |
| 刊名 | KNOWLEDGE AND INFORMATION SYSTEMS
 |
| 出版日期 | 2016-12-01 |
| 卷号 | 49期号:3页码:835-859 |
| 关键词 | Recommender system Heterogeneous information network Matrix factorization Similarity measure |
| ISSN号 | 0219-1377 |
| DOI | 10.1007/s10115-016-0925-0 |
| 英文摘要 | Recently, there is a surge of social recommendation, which leverages social relations among users to improve recommendation performance. However, in many applications, social relations are very sparse or absent. Meanwhile, the attribute information of users or items may be rich. It is a big challenge to exploit this attribute information for the improvement of recommendation performance. In this paper, we organize objects and relations in recommender system as a heterogeneous information network and introduce meta-path-based similarity measure to evaluate the similarity of users or items. Furthermore, a matrix factorization-based dual regularization framework SimMF is proposed to flexibly integrate different types of information through adopting users' and items' similarities as regularization on latent factors of users and items. Extensive experiments not only validate the effectiveness of SimMF but also reveal some interesting findings. We find that attribute information of users and items can significantly improve recommendation accuracy, and their contribution seems more important than that of social relations. The experiments also reveal that different regularization models have obviously different impacts on users and items. |
| 资助项目 | National Key Basic Research and Department (973) Program of China[2013CB329606] ; National Natural Science Foundation of China[71231002] ; National Natural Science Foundation of China[61473273] ; CCF-Tencent Open Fund ; Co-construction Project of Beijing Municipal Commission of Education ; US NSF[III-1526499] ; Microsoft Research Asia Collaborative Research Program |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000386120000002 |
| 出版者 | SPRINGER LONDON LTD |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/8020]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Zhuang, Fuzhen |
| 作者单位 | 1.Univ Illinois, Comp Sci, Chicago, IL USA 2.Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing, Peoples R China 3.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shi, Chuan,Liu, Jian,Zhuang, Fuzhen,et al. Integrating heterogeneous information via flexible regularization framework for recommendation[J]. KNOWLEDGE AND INFORMATION SYSTEMS,2016,49(3):835-859. |
| APA | Shi, Chuan,Liu, Jian,Zhuang, Fuzhen,Yu, Philip S.,&Wu, Bin.(2016).Integrating heterogeneous information via flexible regularization framework for recommendation.KNOWLEDGE AND INFORMATION SYSTEMS,49(3),835-859. |
| MLA | Shi, Chuan,et al."Integrating heterogeneous information via flexible regularization framework for recommendation".KNOWLEDGE AND INFORMATION SYSTEMS 49.3(2016):835-859. |
入库方式： OAI收割
来源：计算技术研究所
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