research-article

DTCDR: A Framework for Dual-Target Cross-Domain Recommendation

Authors:

Xiaolin ZhengAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 1533 - 1542

https://doi.org/10.1145/3357384.3357992

Published: 03 November 2019 Publication History

Abstract

In order to address the data sparsity problem in recommender systems, in recent years, Cross-Domain Recommendation (CDR) leverages the relatively richer information from a source domain to improve the recommendation performance on a target domain with sparser information. However, each of the two domains may be relatively richer in certain types of information (e.g., ratings, reviews, user profiles, item details, and tags), and thus, if we can leverage such information well, it is possible to improve the recommendation performance on both domains simultaneously (i.e., dual-target CDR), rather than a single target domain only. To this end, in this paper, we propose a new framework, DTCDR, for Dual-Target Cross-Domain Recommendation. In DTCDR, we first extensively utilize rating and multi-source content information to generate rating and document embeddings of users and items. Then, based on Multi-Task Learning (MTL), we design an adaptable embedding-sharing strategy to combine and share the embeddings of common users across domains, with which DTCDR can improve the recommendation performance on both richer and sparser (i.e., dual-target) domains simultaneously. Extensive experiments conducted on real-world datasets demonstrate that DTCDR can significantly improve the recommendation accuracies on both richer and sparser domains and outperform the state-of-the-art single-domain and cross-domain approaches.

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Cited By

Tang MCui SJin ZLiang SLi CZou L(2025)Sequential recommendation by reprogramming pretrained transformerInformation Processing & Management10.1016/j.ipm.2024.10393862:1(103938)Online publication date: Jan-2025
https://doi.org/10.1016/j.ipm.2024.103938
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https://doi.org/10.4018/JCIT.355241
WANG QDI YHUANG LWANG GLIU Y(2024)TECDR: Cross-Domain Recommender System Based on Domain Knowledge Transferor and Latent Preference ExtractorIEICE Transactions on Information and Systems10.1587/transinf.2023EDP7175E107.D:5(704-713)Online publication date: 1-May-2024
https://doi.org/10.1587/transinf.2023EDP7175
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Index Terms

DTCDR: A Framework for Dual-Target Cross-Domain Recommendation
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Multi-task 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

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, China

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Published: 03 November 2019

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November 3 - 7, 2019

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Cited By

Tang MCui SJin ZLiang SLi CZou L(2025)Sequential recommendation by reprogramming pretrained transformerInformation Processing & Management10.1016/j.ipm.2024.10393862:1(103938)Online publication date: Jan-2025
https://doi.org/10.1016/j.ipm.2024.103938
Lin LLi HLiu XPang SYuan MLiu X(2024)Graph Agent Transformer Network With Contrast Learning for Cross-Domain Recommendation of E-CommerceJournal of Cases on Information Technology10.4018/JCIT.35524126:1(1-16)Online publication date: 26-Sep-2024
https://doi.org/10.4018/JCIT.355241
WANG QDI YHUANG LWANG GLIU Y(2024)TECDR: Cross-Domain Recommender System Based on Domain Knowledge Transferor and Latent Preference ExtractorIEICE Transactions on Information and Systems10.1587/transinf.2023EDP7175E107.D:5(704-713)Online publication date: 1-May-2024
https://doi.org/10.1587/transinf.2023EDP7175
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
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Sun KLi CQian T(2024)City Matters! A Dual-Target Cross-City Sequential POI Recommendation ModelACM Transactions on Information Systems10.1145/366428442:6(1-27)Online publication date: 19-Aug-2024
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Tian CXie YChen XLi YZhao X(2024)Privacy-preserving Cross-domain Recommendation with Federated Graph LearningACM Transactions on Information Systems10.1145/365344842:5(1-29)Online publication date: 13-May-2024
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Wang YXie QTang MLi LYuan JLiu Y(2024)A Dual Perspective Framework of Knowledge-correlation for Cross-domain RecommendationACM Transactions on Knowledge Discovery from Data10.1145/365252018:6(1-28)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3652520
Lu KZhang QHughes DZhang GLu J(2024)AMT-CDR: A Deep Adversarial Multi-Channel Transfer Network for Cross-Domain RecommendationACM Transactions on Intelligent Systems and Technology10.1145/364128615:4(1-26)Online publication date: 27-Jan-2024
https://dl.acm.org/doi/10.1145/3641286
Liu JShang LSu YNie WWen XLiu A(2024)Privacy-preserving Multi-source Cross-domain Recommendation Based on Knowledge GraphACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363970620:5(1-18)Online publication date: 5-Jan-2024
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Song ZZhang WDeng LZhang JWu ZBian KCui BBaeza-Yates RBonchi F(2024)Mitigating Negative Transfer in Cross-Domain Recommendation via Knowledge Transferability EnhancementProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671799(2745-2754)Online publication date: 25-Aug-2024
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