NDLI: Joint User Modeling across Aligned Heterogeneous Sites

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Joint User Modeling across Aligned Heterogeneous Sites

Content Provider	ACM Digital Library
Author	Cao, Xuezhi Yu, Yong
Abstract	An accurate and comprehensive user modeling technique is crucial for the quality of recommender systems. Traditionally, we model user preferences using only actions from the target site and may suffer from cold-start problem. As nowadays people normally engage in multiple online sites for various needs, we consider leveraging the cross-site actions to improve the user modeling accuracy. Specifically, in this paper we aim at achieving a more comprehensive and accurate user modeling by modeling user's actions in multiple aligned heterogeneous sites simultaneously. To do so, we propose a modularized probabilistic graphical model framework JUMA. We further integrate topic model and matrix factorization into JUMA for joint user modeling over text-based and item-based sites. We assemble and publish large-scale dataset for comprehensive analyzing and evaluation. Experimental results show that our framework JUMA out performs traditional within-site user modeling techniques, especially for cold-start scenarios. For cold-start users, we achieve relative improvements of 9.3% and 12.8% comparing to existing within-site approaches for recommendation in item-based and text-based sites respectively. Thus we draw the conclusion that aligning heterogeneous sites and modeling users jointly do help to improve the quality of online recommender systems.
Starting Page	83
Ending Page	90
Page Count	8
File Format	PDF MP4
ISBN	9781450340359
DOI	10.1145/2959100.2959155
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-09-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Graphical model User modeling Recommender system
Content Type	Audio Text
Resource Type	Article

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