research-article

AutoTransfer: Instance Transfer for Cross-Domain Recommendations

Authors:

Ruiming TangAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1478 - 1487

https://doi.org/10.1145/3539618.3591701

Published: 18 July 2023 Publication History

Abstract

Cross-Domain Recommendation (CDR) is a widely used approach for leveraging information from domains with rich data to assist domains with insufficient data. A key challenge of CDR research is the effective and efficient transfer of helpful information from source domain to target domain. Currently, most existing CDR methods focus on extracting implicit information from the source domain to enhance the target domain. However, the hidden structure of the extracted implicit information is highly dependent on the specific CDR model, and is therefore not easily reusable or transferable. Additionally, the extracted implicit information only appears within the intermediate substructure of specific CDRs during training and is thus not easily retained for more use. In light of these challenges, this paper proposes AutoTransfer, with an Instance Transfer Policy Network, to selectively transfers instances from source domain to target domain for improved recommendations. Specifically, AutoTransfer acts as an agent that adaptively selects a subset of informative and transferable instances from the source domain. Notably, the selected subset possesses extraordinary re-utilization property that can be saved for improving model training of various future RS models in target domain. Experimental results on two public CDR benchmark datasets demonstrate that the proposed method outperforms state-of-the-art CDR baselines and classic Single-Domain Recommendation (SDR) approaches. The implementation code is available for easy reproduction.

References

[1]

Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath. 2017. Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine, Vol. 34, 6 (2017), 26--38.

[2]

Erion cC ano and Maurizio Morisio. 2017. Hybrid recommender systems: A systematic literature review. Intelligent Data Analysis, Vol. 21, 6 (2017), 1487--1524.

Digital Library

[3]

Rui Chen, Qingyi Hua, Yan-Shuo Chang, Bo Wang, Lei Zhang, and Xiangjie Kong. 2018. A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks. IEEE Access, Vol. 6 (2018), 64301--64320.

[4]

Aminu Da'u and Naomie Salim. 2020. Recommendation system based on deep learning methods: a systematic review and new directions. Artificial Intelligence Review, Vol. 53, 4 (2020), 2709--2748.

[5]

Ignacio Fernández-Tobías, Iván Cantador, Paolo Tomeo, Vito Walter Anelli, and Tommaso Di Noia. 2019. Addressing the user cold start with cross-domain collaborative filtering: exploiting item metadata in matrix factorization. User Modeling and User-Adapted Interaction, Vol. 29 (2019), 443--486.

Digital Library

[6]

Sheng Gao, Hao Luo, Da Chen, Shantao Li, Patrick Gallinari, and Jun Guo. 2013. Cross-domain recommendation via cluster-level latent factor model. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II 13. Springer, 161--176.

[7]

Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 1725--1731.

[8]

Lei Guo, Li Tang, Tong Chen, Lei Zhu, Quoc Viet Hung Nguyen, and Hongzhi Yin. 2021. DA-GCN: a domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation. In IJCAI International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2483--2489.

[9]

Wenxing Hong, Nannan Zheng, Ziang Xiong, and Zhiqiang Hu. 2020. A parallel deep neural network using reviews and item metadata for cross-domain recommendation. IEEE Access, Vol. 8 (2020), 41774--41783.

[10]

PN Vijaya Kumar and V Raghunatha Reddy. 2014. A survey on recommender systems (RSS) and its applications. International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, 8 (2014), 5254--5260.

[11]

Matevvz Kunaver and Tomavz Povz rl. 2017. Diversity in recommender systems--A survey. Knowledge-based systems, Vol. 123 (2017), 154--162.

[12]

Sangjae Lee and Joon Yeon Choeh. 2014. Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Systems with Applications, Vol. 41, 6 (2014), 3041--3046.

Digital Library

[13]

Bin Li. 2011. Cross-domain collaborative filtering: A brief survey. In 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence. IEEE, 1085--1086.

Digital Library

[14]

Pan Li and Alexander Tuzhilin. 2020. Ddtcdr: Deep dual transfer cross domain recommendation. In Proceedings of the 13th International Conference on Web Search and Data Mining. 331--339.

Digital Library

[15]

Weilin Lin, Xiangyu Zhao, Yejing Wang, Tong Xu, and Xian Wu. 2022. AdaFS: Adaptive Feature Selection in Deep Recommender System. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3309--3317.

Digital Library

[16]

Weilin Lin, Xiangyu Zhao, Yejing Wang, Yuanshao Zhu, and Wanyu Wang. 2023. AutoDenoise: Automatic Data Instance Denoising for Recommendations. In Proceedings of the Web Conference 2023.

Digital Library

[17]

Jie Lu, Dianshuang Wu, Mingsong Mao, Wei Wang, and Guangquan Zhang. 2015. Recommender system application developments: a survey. Decision Support Systems, Vol. 74 (2015), 12--32.

Digital Library

[18]

Zhongqi Lu, Erheng Zhong, Lili Zhao, Evan Wei Xiang, Weike Pan, and Qiang Yang. 2013. Selective transfer learning for cross domain recommendation. In Proceedings of the 2013 SIAM International Conference on Data Mining. SIAM, 641--649.

[19]

Chen Qu, Feng Ji, Minghui Qiu, Liu Yang, Zhiyu Min, Haiqing Chen, Jun Huang, and W Bruce Croft. 2019. Learning to selectively transfer: Reinforced transfer learning for deep text matching. In Proceedings of the twelfth ACM international conference on web search and data mining. 699--707.

Digital Library

[20]

Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang. 2016. Product-based neural networks for user response prediction. In 2016 IEEE 16th international conference on data mining (ICDM). IEEE, 1149--1154.

[21]

Arti Rana, Arvind Singh Rawat, Anchit Bijalwan, and Himanshu Bahuguna. 2018. Application of multi layer (perceptron) artificial neural network in the diagnosis system: a systematic review. In 2018 International conference on research in intelligent and computing in engineering (RICE). IEEE, 1--6.

[22]

Usha Ruby and Vamsidhar Yendapalli. 2020. Binary cross entropy with deep learning technique for image classification. Int. J. Adv. Trends Comput. Sci. Eng, Vol. 9, 10 (2020).

[23]

Ajit P Singh and Geoffrey J Gordon. 2008. Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 650--658.

Digital Library

[24]

Brent Smith and Greg Linden. 2017. Two decades of recommender systems at Amazon. com. Ieee internet computing, Vol. 21, 3 (2017), 12--18.

[25]

Mehdi Srifi, Ahmed Oussous, Ayoub Ait Lahcen, and Salma Mouline. 2020. Recommender systems based on collaborative filtering using review texts-a survey. Information, Vol. 11, 6 (2020), 317.

[26]

Anh-Phuong Ta. 2015. Factorization machines with follow-the-regularized-leader for CTR prediction in display advertising. In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2889--2891.

Digital Library

[27]

Bo Wang, Minghui Qiu, Xisen Wang, Yaliang Li, Yu Gong, Xiaoyi Zeng, Jun Huang, Bo Zheng, Deng Cai, and Jingren Zhou. 2019. A minimax game for instance based selective transfer learning. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 34--43.

Digital Library

[28]

Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017. Deep & cross network for ad click predictions. In Proceedings of the ADKDD'17. 1--7.

Digital Library

[29]

Yejing Wang, Xiangyu Zhao, Tong Xu, and Xian Wu. 2022. Autofield: Automating feature selection in deep recommender systems. In Proceedings of the ACM Web Conference 2022. 1977--1986.

Digital Library

[30]

Ronald J Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, Vol. 8, 3 (1992), 229--256.

Digital Library

[31]

Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua. 2017. Attentional factorization machines: learning the weight of feature interactions via attention networks. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 3119--3125.

[32]

Zhibo Xiao, Luwei Yang, Wen Jiang, Yi Wei, Yi Hu, and Hao Wang. 2020. Deep multi-interest network for click-through rate prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2265--2268.

Digital Library

[33]

Xin Xin, Tiago Pimentel, Alexandros Karatzoglou, Pengjie Ren, Konstantina Christakopoulou, and Zhaochun Ren. 2022. Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1347--1357.

Digital Library

[34]

Xu Yu, Qiang Hu, Hui Li, Junwei Du, Jia Gao, and Lijun Sun. 2022. Cross-domain recommendation based on latent factor alignment. Neural Computing and Applications (2022), 1--12.

[35]

Feng Yuan, Lina Yao, and Boualem Benatallah. 2019. DARec: deep domain adaptation for cross-domain recommendation via transferring rating patterns. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 4227--4233.

[36]

Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR), Vol. 52, 1 (2019), 1--38.

Digital Library

[37]

Weinan Zhang, Tianming Du, and Jun Wang. 2016. Deep learning over multi-field categorical data. In European conference on information retrieval. Springer, 45--57.

[38]

Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Kaiyuan Li, Yushuo Chen, Yujie Lu, Hui Wang, Changxin Tian, Xingyu Pan, Yingqian Min, Zhichao Feng, Xinyan Fan, Xu Chen, Pengfei Wang, Wendi Ji, Yaliang Li, Xiaoling Wang, and Ji-Rong Wen. 2021d. Recbole: Towards a unified, comprehensive and efficient framework for recommendation algorithms. In CIKM.

[39]

Xiangyu Zhao. 2022. Adaptive and automated deep recommender systems. ACM SIGWEB Newsletter Spring (2022), 1--4.

[40]

Xiangyu Zhao, Changsheng Gu, Haoshenglun Zhang, Xiwang Yang, Xiaobing Liu, Hui Liu, and Jiliang Tang. 2021a. DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 750--758.

[41]

Xiangyu Zhao, Haochen Liu, Wenqi Fan, Hui Liu, Jiliang Tang, Chong Wang, Ming Chen, Xudong Zheng, Xiaobing Liu, and Xiwang Yang. 2021b. Autoemb: Automated embedding dimensionality search in streaming recommendations. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 896--905.

[42]

Xiangyu Zhao, Haochen Liu, Hui Liu, Jiliang Tang, Weiwei Guo, Jun Shi, Sida Wang, Huiji Gao, and Bo Long. 2021c. AutoDim: Field-aware Embedding Dimension Searchin Recommender Systems. In Proceedings of the Web Conference 2021. 3015--3022.

Digital Library

[43]

Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin. 2019. Deep reinforcement learning for search, recommendation, and online advertising: a survey. ACM SIGWEB Newsletter Spring (2019), 1--15.

[44]

Feng Zhu, Chaochao Chen, Yan Wang, Guanfeng Liu, and Xiaolin Zheng. 2019. Dtcdr: A framework for dual-target cross-domain recommendation. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1533--1542.

Digital Library

[45]

Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, and Guanfeng Liu. 2021. Cross-domain recommendation: challenges, progress, and prospects. The 30th International Joint Conference on Artificial Intelligence (IJCAI) (2021).

Cited By

Liu QQiu ZZhao XWu XZhang ZXu TTian F(2025)A Contrastive Pretrain Model with Prompt Tuning for Multi-center Medication RecommendationACM Transactions on Information Systems10.1145/370663143:3(1-29)Online publication date: 3-Jan-2025
https://dl.acm.org/doi/10.1145/3706631
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
Wang YSerra ESpezzano F(2024)Multi-Granularity Modeling in Recommendation: from the Multi-Scenario PerspectiveProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680264(5491-5494)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680264
Show More Cited By

Index Terms

AutoTransfer: Instance Transfer for Cross-Domain Recommendations
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Robust Transfer Learning for Cross-domain Collaborative Filtering Using Multiple Rating Patterns Approximation
WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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 ...
A deep framework for cross-domain and cross-system recommendations
IJCAI'18: Proceedings of the 27th International Joint Conference on Artificial Intelligence

Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender systems. They leverage the relatively richer information, e.g., ratings, from ...
Entity knowledge transfer-oriented dual-target cross-domain recommendations
Highlights
- A new entity embedding based on entity interaction and side information is proposed.
Abstract
Cross-domain recommendations can assist users in selecting suitable items in the target domain by aggregating or transferring the abundant available data from the auxiliary domain, which has gradually become a promising research area. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

Copyright © 2023 ACM.

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 the author(s) 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].

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 July 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

SIRG - CityU Strategic Interdisciplinary Research Grant
CityU - HKIDS Early Career Research Grant
APRC - CityU New Research Initiatives
Huawei (Huawei Innovation Research Program)

Conference

SIGIR '23

Sponsor:

SIGIR

SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
708
Total Downloads

Downloads (Last 12 months)179
Downloads (Last 6 weeks)6

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Liu QQiu ZZhao XWu XZhang ZXu TTian F(2025)A Contrastive Pretrain Model with Prompt Tuning for Multi-center Medication RecommendationACM Transactions on Information Systems10.1145/370663143:3(1-29)Online publication date: 3-Jan-2025
https://dl.acm.org/doi/10.1145/3706631
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
Wang YSerra ESpezzano F(2024)Multi-Granularity Modeling in Recommendation: from the Multi-Scenario PerspectiveProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680264(5491-5494)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680264
Xu SGuan DSerra ESpezzano F(2024)CrossPred: A Cross-City Mobility Prediction Framework for Long-Distance Travelers via POI Feature MatchingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679893(4148-4152)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679893
Gao JChen BZhu MZhao XLi XWang YWang YGuo HTang RSerra ESpezzano F(2024)HierRec: Scenario-Aware Hierarchical Modeling for Multi-scenario RecommendationsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679615(653-662)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679615
Hou CZhou YCao YLiu THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661348(2885-2889)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661348
Li XZhao XWang ZDuan YZhang YXing CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Optimal Transport Enhanced Cross-City Site RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657757(1441-1451)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657757
Zhang ZLiu SYu JCai QZhao XZhang CLiu ZLiu QZhao HHu LJiang PGai KHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation FrameworkProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657686(893-902)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657686
Tang YRahmani Dehaghani MSajadi PWang G(2024)Selecting subsets of source data for transfer learning with applications in metal additive manufacturingJournal of Intelligent Manufacturing10.1007/s10845-024-02402-6Online publication date: 12-May-2024
https://doi.org/10.1007/s10845-024-02402-6
Li XYan FZhao XWang YChen BGuo HTang RFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)HAMUR: Hyper Adapter for Multi-Domain RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615137(1268-1277)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615137
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten