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Low-dimensional Alignment for Cross-Domain Recommendation

Authors:

Qing HeAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 3508 - 3512

https://doi.org/10.1145/3459637.3482137

Published: 30 October 2021 Publication History

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Abstract

Cold start problem is one of the most challenging and long-standing problems in recommender systems, and cross-domain recommendation (CDR) methods are effective for tackling it. Most cold-start related CDR methods require training a mapping function between high-dimensional embedding space using overlapping user data. However, the overlapping data is scarce in many recommendation tasks, which makes it difficult to train the mapping function. In this paper, we propose a new approach for CDR, which aims to alleviate the training difficulty. The proposed method can be viewed as a special parameterization of the mapping function without hurting expressiveness, which makes use of non-overlapping user data and leads to effective optimization. Extensive experiments on two real-world CDR tasks are performed to evaluate the proposed method. In the case that there are few overlapping data, the proposed method outperforms the existed state-of-the-art method by 14% (relative improvement).

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Cited By

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Tang JFeng XChen X(2025)Fairness-aware Cross-Domain RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_19(293-302)Online publication date: 12-Jan-2025
https://doi.org/10.1007/978-981-97-5555-4_19
Ma HXie RMeng LChen XZhang XLin LZhou J(2024)Triple Sequence Learning for Cross-domain RecommendationACM Transactions on Information Systems10.1145/363835142:4(1-29)Online publication date: 9-Feb-2024
https://dl.acm.org/doi/10.1145/3638351
Du JYe ZGuo BYu ZYao LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Identifiability of Cross-Domain Recommendation via Causal Subspace DisentanglementProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657758(2091-2101)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657758
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Index Terms

Low-dimensional Alignment for Cross-Domain Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
2. Information systems
  1. Information retrieval

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Published In

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 October 2021

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Short-paper

Funding Sources

The research work supported by the National Natural Science Foun- dation of China under Grant No. U1836206, U1811461, 61773361 and CCF-Ant Research Fund.

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CIKM '21

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CIKM '21: The 30th ACM International Conference on Information and Knowledge Management

November 1 - 5, 2021

Queensland, Virtual Event, Australia

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

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Cited By

View all

Tang JFeng XChen X(2025)Fairness-aware Cross-Domain RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_19(293-302)Online publication date: 12-Jan-2025
https://doi.org/10.1007/978-981-97-5555-4_19
Ma HXie RMeng LChen XZhang XLin LZhou J(2024)Triple Sequence Learning for Cross-domain RecommendationACM Transactions on Information Systems10.1145/363835142:4(1-29)Online publication date: 9-Feb-2024
https://dl.acm.org/doi/10.1145/3638351
Du JYe ZGuo BYu ZYao LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Identifiability of Cross-Domain Recommendation via Causal Subspace DisentanglementProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657758(2091-2101)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657758
Zhou ZLin LWang HZhou XWei GWang SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)A Cross Domain Method for Customer Lifetime Value Prediction in Supply Chain PlatformProceedings of the ACM Web Conference 202410.1145/3589334.3645391(4037-4046)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645391
Liu WChen CLiao XHu MSu JTan YWang FChua TNgo CKa-Wei Lee RKumar RLauw H(2024)User Distribution Mapping Modelling with Collaborative Filtering for Cross Domain RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645331(334-343)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645331
Zang TZhu YZhang RZhu JTang F(2024)Explicitly modeling relationships between domain-specific and domain-invariant interests for cross-domain recommendationWorld Wide Web10.1007/s11280-024-01305-z27:6Online publication date: 28-Oct-2024
https://doi.org/10.1007/s11280-024-01305-z
Zhang FZhou YSun PXu YHan WHuang HChen J(2024)CRAS: cross-domain recommendation via aspect-level sentiment extractionKnowledge and Information Systems10.1007/s10115-024-02130-666:9(5459-5477)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s10115-024-02130-6
Zang TZhu YZhang RWang CWang KYu J(2023)Contrastive Multi-view Interest Learning for Cross-domain Sequential RecommendationACM Transactions on Information Systems10.1145/363240242:3(1-30)Online publication date: 29-Dec-2023
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Ma HXie RMeng LChen XZhang XLin LZhou J(2023)Exploring False Hard Negative Sample in Cross-Domain RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608791(502-514)Online publication date: 14-Sep-2023
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Zhao CZhao HHE MZhang JFan J(2023)Cross-domain recommendation via user interest alignmentProceedings of the ACM Web Conference 202310.1145/3543507.3583263(887-896)Online publication date: 30-Apr-2023
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