research-article

Robust Transfer Learning for Cross-domain Collaborative Filtering Using Multiple Rating Patterns Approximation

Authors:

Kaisheng YaoAuthors Info & Claims

WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

Pages 225 - 233

https://doi.org/10.1145/3159652.3159675

Published: 02 February 2018 Publication History

Abstract

Collaborative filtering techniques are a common approach for building recommendations, and have been widely applied in real recommender systems. However, collaborative filtering usually suffers from limited performance due to the sparsity of user-item interaction. To address this issue, auxiliary information is usually used to improve the performance. Transfer learning provides the key idea of using knowledge from auxiliary domains. An assumption of transfer learning in collaborative filtering is that the source domain is a full rating matrix, which may not hold in many real-world applications. In this paper, we investigate how to leverage rating patterns from multiple incomplete source domains to improve the quality of recommender systems. First, by exploiting the transferred learning, we compress the knowledge from the source domain into a cluster-level rating matrix. The rating patterns in the low-level matrix can be transferred to the target domain. Specifically, we design a knowledge extraction method to enrich rating patterns by relaxing the full rating restriction on the source domain. Finally, we propose a robust multiple-rating-pattern transfer learning model for cross-domain collaborative filtering, which is called MINDTL, to accurately predict missing values in the target domain. Extensive experiments on real-world datasets demonstrate that our proposed approach is effective and outperforms several alternative methods.

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Cited By

Fu ZNiu XWu XRahman R(2024)A Deep Learning Model for Cross-Domain Serendipity RecommendationsACM Transactions on Recommender Systems10.1145/3690654Online publication date: 29-Aug-2024
https://doi.org/10.1145/3690654
Zhu FYang XLi LZhou JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)An Active Masked Attention Framework for Many-to-Many Cross-Domain RecommendationsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681435(9680-9689)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681435
Huang LLi WZhang CWang JYi XChen SSerra ESpezzano F(2024)EXIT: An EXplicit Interest Transfer Framework for Cross-Domain RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680055(4563-4570)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680055
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Index Terms

Robust Transfer Learning for Cross-domain Collaborative Filtering Using Multiple Rating Patterns Approximation
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Multi-task learning
        Transfer learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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cover image ACM Conferences

WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

February 2018

821 pages

ISBN:9781450355810

DOI:10.1145/3159652

General Chairs:
Yi Chang
Jilin University, Huawei Inc.
,
Chengxiang Zhai
University of Illinois Urbana-Champaign
,
Program Chairs:
Yan Liu
University of Southern California
,
Yoelle Maarek
Amazon

Copyright © 2018 ACM.

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Published: 02 February 2018

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Key Project of Beijing Municipal Education Commission
General Project of Beijing Municipal Education Commission
National Natural Science Foundation of China

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WSDM 2018

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WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining

February 5 - 9, 2018

CA, Marina Del Rey, USA

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WSDM '18 Paper Acceptance Rate 81 of 514 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Fu ZNiu XWu XRahman R(2024)A Deep Learning Model for Cross-Domain Serendipity RecommendationsACM Transactions on Recommender Systems10.1145/3690654Online publication date: 29-Aug-2024
https://doi.org/10.1145/3690654
Zhu FYang XLi LZhou JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)An Active Masked Attention Framework for Many-to-Many Cross-Domain RecommendationsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681435(9680-9689)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681435
Huang LLi WZhang CWang JYi XChen SSerra ESpezzano F(2024)EXIT: An EXplicit Interest Transfer Framework for Cross-Domain RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680055(4563-4570)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680055
Wang YXie QTang MLi LYuan JLiu YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Amazon-KG: A Knowledge Graph Enhanced Cross-Domain Recommendation DatasetProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657880(123-130)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657880
Zhao CZhao HLi XHe MWang JFan J(2024)Cross-Domain Recommendation via Progressive Structural AlignmentIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3324912(1-16)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3324912
Xie YYu CJin XCheng LHu BLi Z(2024)Heterogeneous graph contrastive learning for cold start cross-domain recommendationKnowledge-Based Systems10.1016/j.knosys.2024.112054299(112054)Online publication date: Sep-2024
https://doi.org/10.1016/j.knosys.2024.112054
Xu ZPan WMing Z(2024)Transfer Learning in Cross-Domain Sequential RecommendationInformation Sciences10.1016/j.ins.2024.120550(120550)Online publication date: Apr-2024
https://doi.org/10.1016/j.ins.2024.120550
Liu HHe JXu ZZhang YZhao Z(2024)Efficient and adaptive secure cross-domain recommendationsExpert Systems with Applications10.1016/j.eswa.2024.125154(125154)Online publication date: Aug-2024
https://doi.org/10.1016/j.eswa.2024.125154
Hu KQiu LZhang SWang ZFang NZhou H(2023)A novel neighbor selection scheme based on dynamic evaluation towards recommender systemsScience Progress10.1177/00368504231180090106:2Online publication date: 8-Jun-2023
https://doi.org/10.1177/00368504231180090
Li YHou LLi J(2023)Preference-aware Graph Attention Networks for Cross-Domain Recommendations with Collaborative Knowledge GraphACM Transactions on Information Systems10.1145/357692141:3(1-26)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1145/3576921
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