research-article

DDTCDR: Deep Dual Transfer Cross Domain Recommendation

Authors:

Alexander TuzhilinAuthors Info & Claims

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

Pages 331 - 339

https://doi.org/10.1145/3336191.3371793

Published: 22 January 2020 Publication History

Abstract

Cross domain recommender systems have been increasingly valuable for helping consumers identify the most satisfying items from different categories. However, previously proposed cross-domain models did not take into account bidirectional latent relations between users and items. In addition, they do not explicitly model information of user and item features, while utilizing only user ratings information for recommendations. To address these concerns, in this paper we propose a novel approach to cross-domain recommendations based on the mechanism of dual learning that transfers information between two related domains in an iterative manner until the learning process stabilizes. We develop a novel latent orthogonal mapping to extract user preferences over multiple domains while preserving relations between users across different latent spaces. Combining with autoencoder approach to extract the latent essence of feature information, we propose Deep Dual Transfer Cross Domain Recommendation (DDTCDR) model to provide recommendations in respective domains. We test the proposed method on a large dataset containing three domains of movies, book and music items and demonstrate that it consistently and significantly outperforms several state-of-the-art baselines and also classical transfer learning approaches.

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Cited By

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: 16-Oct-2024
https://dl.acm.org/doi/10.4018/JCIT.355241
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
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
Show More Cited By

Index Terms

DDTCDR: Deep Dual Transfer Cross Domain Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

January 2020

950 pages

ISBN:9781450368223

DOI:10.1145/3336191

General Chairs:
James Caverlee
Texas A&M University
,
Xia "Ben" Hu
Texas A&M University
,
Program Chairs:
Mounia Lalmas
Spotify
,
Wei Wang
University of California, Los Angeles

Copyright © 2020 ACM.

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Published: 22 January 2020

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WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining

February 3 - 7, 2020

TX, Houston, USA

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Cited By

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: 16-Oct-2024
https://dl.acm.org/doi/10.4018/JCIT.355241
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
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
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
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
https://dl.acm.org/doi/10.1145/3664284
Liu HGuo LZhu LJiang YGao MYin H(2024)MCRPL: A Pretrain, Prompt, and Fine-tune Paradigm for Non-overlapping Many-to-one Cross-domain RecommendationACM Transactions on Information Systems10.1145/364186042:4(1-24)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1145/3641860
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
Wang JRathi PSundaram H(2024)A Pre-trained Zero-shot Sequential Recommendation Framework via Popularity DynamicsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688145(433-443)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688145
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
Zhao SSahebi S(2024)Discerning Canonical User Representation for Cross-Domain RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688114(318-328)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688114
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