Knowledge Transfer among Heterogeneous Information Networks

被引：1

作者：

Xiang, Evan Wei ^{[1
]}

Liu, Nathan N. ^{[1
]}

Pan, Sinno Jialin ^{[1
]}

Yang, Qiang ^{[1
]}

机构：

[1] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Kowloon, Hong Kong, Peoples R China

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2009) | 2009年

关键词：

D O I：

10.1109/ICDMW.2009.100

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Online recommendation systems are becoming more and more popular with the development of web. However, a critical problem of such system is that new users and items are always added to the system with time. How to overcome the data sparseness for such new incoming entities become an important issue. In this paper, we try to reduce the data sparseness in the link prediction problem via involving heterogeneous information network as auxiliary information sources. We developed two models based on the Collective Matrix Factorization (CMF) framework. We also provided a detailed empirical study on how effectively different information networks could help with two real world link prediction tasks. We will report some preliminary results of our current work and also point our several potential research issues.

引用

页码：429 / 434

页数：6

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