research-article

Learning Informative Priors from Heterogeneous Domains to Improve Recommendation in Cold-Start User Domains

Authors:

Dingyu YangAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 35, Issue 2

Article No.: 13, Pages 1 - 37

https://doi.org/10.1145/2976737

Published: 12 December 2016 Publication History

Abstract

In the real-world environment, users have sufficient experience in their focused domains but lack experience in other domains. Recommender systems are very helpful for recommending potentially desirable items to users in unfamiliar domains, and cross-domain collaborative filtering is therefore an important emerging research topic. However, it is inevitable that the cold-start issue will be encountered in unfamiliar domains due to the lack of feedback data. The Bayesian approach shows that priors play an important role when there are insufficient data, which implies that recommendation performance can be significantly improved in cold-start domains if informative priors can be provided. Based on this idea, we propose a Weighted Irregular Tensor Factorization (WITF) model to leverage multi-domain feedback data across all users to learn the cross-domain priors w.r.t. both users and items. The features learned from WITF serve as the informative priors on the latent factors of users and items in terms of weighted matrix factorization models. Moreover, WITF is a unified framework for dealing with both explicit feedback and implicit feedback. To prove the effectiveness of our approach, we studied three typical real-world cases in which a collection of empirical evaluations were conducted on real-world datasets to compare the performance of our model and other state-of-the-art approaches. The results show the superiority of our model over comparison models.

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Index Terms

Learning Informative Priors from Heterogeneous Domains to Improve Recommendation in Cold-Start User Domains
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 35, Issue 2

April 2017

232 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3001595

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

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Copyright © 2016 ACM.

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Association for Computing Machinery

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Publication History

Published: 12 December 2016

Accepted: 01 July 2016

Revised: 01 May 2016

Received: 01 August 2015

Published in TOIS Volume 35, Issue 2

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National Natural Science Foundation of China
Research Funds of Science and Technology Commission of Shanghai Municipality
Australia Research Council Linkage Project

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https://doi.org/10.1109/TETCI.2024.3378599
He PShi JMa WZheng X(2024)Broad collaborative filtering with adjusted cosine similarity by fusing matrix completionApplied Soft Computing10.1016/j.asoc.2024.112075165(112075)Online publication date: Nov-2024
https://doi.org/10.1016/j.asoc.2024.112075
Yi MLiu MFeng CDeng W(2023)A cross-domain recommendation model by unified modelling high-order information and rating informationJournal of Information Science10.1177/01655515231182068Online publication date: 8-Jul-2023
https://doi.org/10.1177/01655515231182068
Su HLi JDu ZZhu LLu KShen H(2023)Cross-domain Recommendation via Dual Adversarial AdaptationACM Transactions on Information Systems10.1145/363252442:3(1-26)Online publication date: 11-Nov-2023
https://dl.acm.org/doi/10.1145/3632524
Shen YZhao LCheng WZhang ZZhou WKangyi L(2023)RESUS: Warm-up Cold Users via Meta-learning Residual User Preferences in CTR PredictionACM Transactions on Information Systems10.1145/356428341:3(1-26)Online publication date: 7-Feb-2023
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Yuan HHernandez A(2023)User Cold Start Problem in Recommendation Systems: A Systematic ReviewIEEE Access10.1109/ACCESS.2023.333870511(136958-136977)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3338705
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Panda DRay S(2022)Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature reviewJournal of Intelligent Information Systems10.1007/s10844-022-00698-559:2(341-366)Online publication date: 23-Apr-2022
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