research-article

A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions

Authors:

Jiadi YuAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 2

Article No.: 42, Pages 1 - 39

https://doi.org/10.1145/3548455

Published: 21 December 2022 Publication History

Abstract

Traditional recommendation systems are faced with two long-standing obstacles, namely data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information collected from other domains to alleviate the two problems in one domain. Since the early 2010s, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged. However, there is a limited number of systematic surveys on CDR, especially regarding the latest proposed methods as well as the recommendation scenarios and recommendation tasks they address. In this survey article, we first proposed a two-level taxonomy of cross-domain recommendation that classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. We conclude this survey by providing several potential research directions about this field.

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Cited By

Acharyya SPervin N(2025)Enhancing cross-domain recommendations: Leveraging personality-based transfer learning with probabilistic matrix factorizationExpert Systems with Applications10.1016/j.eswa.2024.125667263(125667)Online publication date: Mar-2025
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Zhang WZhang JZhang Y(2024)A Comment Aspect-Level User Preference Transfer Model for Cross-Domain RecommendationsInformation Resources Management Journal10.4018/IRMJ.34536037:1(1-25)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.4018/IRMJ.345360
Zhang YZhang ZWu YSun YZhuang FYu WHu LLi HGai KAn ZXu YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Tag Tree-Guided Multi-grained Alignment for Multi-Domain Short Video RecommendationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681692(5683-5691)Online publication date: 28-Oct-2024
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Index Terms

A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 41, Issue 2

April 2023

770 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3568971

Editor:
Min Zhang
Tsinghua University, China

Issue’s Table of Contents

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Publication History

Published: 21 December 2022

Online AM: 11 July 2022

Accepted: 28 June 2022

Revised: 21 March 2022

Received: 16 August 2021

Published in TOIS Volume 41, Issue 2

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2030 National Key AI Program of China
National Science Foundation of China
Shanghai Municipal Science and Technology Commission
Oceanic Interdisciplinary Program of Shanghai Jiao Tong University
Scientific Research Fund of Second Institute of Oceanography
open fund of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, MNR, GE China
Zhejiang Aoxin Co. Ltd.

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Cited By

Acharyya SPervin N(2025)Enhancing cross-domain recommendations: Leveraging personality-based transfer learning with probabilistic matrix factorizationExpert Systems with Applications10.1016/j.eswa.2024.125667263(125667)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.125667
Zhang WZhang JZhang Y(2024)A Comment Aspect-Level User Preference Transfer Model for Cross-Domain RecommendationsInformation Resources Management Journal10.4018/IRMJ.34536037:1(1-25)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.4018/IRMJ.345360
Zhang YZhang ZWu YSun YZhuang FYu WHu LLi HGai KAn ZXu YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Tag Tree-Guided Multi-grained Alignment for Multi-Domain Short Video RecommendationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681692(5683-5691)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681692
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
Shi HLiu MMu XSong XHu YNie L(2024)Breaking Through the Noisy Correspondence: A Robust Model for Image-Text MatchingACM Transactions on Information Systems10.1145/366273242:6(1-26)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3662732
Samra AFrolov EVasilev AGrigorevskiy AVakhrushev A(2024)Cross-Domain Latent Factors Sharing via Implicit Matrix FactorizationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688143(309-317)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688143
Yang YChen BZhu CZhu MDai XGuo HZhang MDong ZTang R(2024)AIE: Auction Information Enhanced Framework for CTR Prediction in Online AdvertisingProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688136(633-642)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688136
Chen JZhang LYang N(2024)Improving Adversarial Robustness for Recommendation Model via Cross-Domain Distributional Adversarial TrainingProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688116(278-286)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688116
Chen GSun RJiang YCao JZhang QLin JLi HGai KZhang X(2024)A Multi-modal Modeling Framework for Cold-start Short-video RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688098(391-400)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688098
Kolb T(2024)Enhancing Cross-Domain Recommender Systems with LLMs: Evaluating Bias and Beyond-Accuracy MeasuresProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688027(1388-1394)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688027
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