Modeling Domains as Distributions with Uncertainty for Cross-Domain Recommendation
Authors: 
Xianghui Zhu, Mengqun Jin, Hengyu Zhang, Chang Meng, Daoxin Zhang, Xiu LiAuthors Info & Claims
Xianghui Zhu, Mengqun Jin, Hengyu Zhang, Chang Meng, Daoxin Zhang, Xiu LiAuthors Info & ClaimsPages 2517 - 2521
Abstract
In the field of dual-target Cross-Domain Recommendation (DTCDR), improving the performance in both the information sparse domain and rich domain has been a mainstream research trend. However, prior embedding-based methods are insufficient to adequately describe the dynamics of user actions and items across domains. Moreover, previous efforts frequently lacked a comprehensive investigation of the entire domain distributions. This paper proposes a novel framework entitled Wasserstein Cross-Domain Recommendation (WCDR) that captures uncertainty in Wasserstein space to address above challenges. In this framework, we abstract user/item actions as Elliptical Gaussian distributions and divide them into local-intrinsic and global-domain parts. To further model the domain diversity, we adopt shared-specific pattern for global-domain distributions and present Masked Domain-aware Sub-distribution Aggregation (MDSA) module to produce informative and diversified global-domain distributions, which incorporates attention-based aggregation method and masking strategy that alleviates negative transfer issues. Extensive experiments on two public datasets and one business dataset are conducted. Experimental results demonstrate the superiority of WCDR over state-of-the-art methods.
References
[1]
Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein generative adversarial networks. In International conference on machine learning. PMLR, 214--223.
[2]
Jiangxia Cao, Jiawei Sheng, Xin Cong, Tingwen Liu, and Bin Wang. 2022. Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck. In IEEE International Conference on Data Engineering (ICDE).
[3]
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al. 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems. 7--10.
[4]
Philippe Clement and Wolfgang Desch. 2008. An elementary proof of the triangle inequality for the Wasserstein metric. Proc. Amer. Math. Soc., Vol. 136, 1 (2008), 333--339.
[5]
Ignacio Fernández-Tob'ias, Iván Cantador, Marius Kaminskas, and Francesco Ricci. 2012. Cross-domain recommender systems: A survey of the state of the art. In Spanish conference on information retrieval, Vol. 24. sn.
[6]
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017).
[7]
Ming He, Jiuling Zhang, Peng Yang, and Kaisheng Yao. 2018. Robust transfer learning for cross-domain collaborative filtering using multiple rating patterns approximation. In ICDM. 225--233.
[8]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. 173--182.
[9]
Guangneng Hu, Yu Zhang, and Qiang Yang. 2018a. Conet: Collaborative cross networks for cross-domain recommendation. In CIKM. 667--676.
[10]
Guangneng Hu, Yu Zhang, and Qiang Yang. 2018b. MTNet: a neural approach for cross-domain recommendation with unstructured text. KDD Deep Learning Day (2018), 1--10.
[11]
Pan Li and Alexander Tuzhilin. 2020. Ddtcdr: Deep dual transfer cross domain recommendation. In ICDM. 331--339.
[12]
Xinhang Li, Zhaopeng Qiu, Xiangyu Zhao, Zihao Wang, Yong Zhang, Chunxiao Xing, and Xian Wu. 2022. Gromov-Wasserstein Guided Representation Learning for Cross-Domain Recommendation. In CIKM. 1199--1208.
[13]
Tong Man, Huawei Shen, Xiaolong Jin, and Xueqi Cheng. 2017. Cross-domain recommendation: An embedding and mapping approach. In IJCAI, Vol. 17. 2464--2470.
[14]
Linh Nguyen and Tsukasa Ishigaki. 2018. Domain-to-domain translation model for recommender system. arXiv preprint arXiv:1812.06229 (2018).
[15]
Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP). 188--197.
[16]
Victor M Panaretos and Yoav Zemel. 2019a. Statistical aspects of Wasserstein distances. Annual review of statistics and its application, Vol. 6 (2019), 405--431.
[17]
Victor M Panaretos and Yoav Zemel. 2019b. Statistical aspects of Wasserstein distances. Annual review of statistics and its application, Vol. 6 (2019), 405--431.
[18]
Dilruk Perera and Roger Zimmermann. 2019. CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped users. In The World Wide Web Conference. 3144--3150.
[19]
Joram Soch, The Book of Statistical Proofs, Thomas J. Faulkenberry, Kenneth Petrykowski, and Carsten Allefeld. 2020. StatProofBook/StatProofBook.github.io: StatProofBook 2020. https://doi.org/10.5281/zenodo.4305950
[20]
Qingquan Song, Dehua Cheng, Hanning Zhou, Jiyan Yang, Yuandong Tian, and Xia Hu. 2020. Towards automated neural interaction discovery for click-through rate prediction. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 945--955.
[21]
Hongzu Su, Yifei Zhang, Xuejiao Yang, Hua Hua, Shuangyang Wang, and Jingjing Li. 2022. Cross-domain Recommendation via Adversarial Adaptation. In CIKM. 1808--1817.
[22]
Jie Tang, Sen Wu, Jimeng Sun, and Hang Su. 2012. Cross-domain collaboration recommendation. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. 1285--1293.
[23]
Yichao Wang, Huifeng Guo, Bo Chen, Weiwen Liu, Zhirong Liu, Qi Zhang, Zhicheng He, Hongkun Zheng, Weiwei Yao, Muyu Zhang, et al. 2022. CausalInt: Causal Inspired Intervention for Multi-Scenario Recommendation. In SIGKDD. 4090--4099.
[24]
Kexin Zhang, Yichao Wang, Xiu Li, Ruiming Tang, and Rui Zhang. 2024. IncMSR: An Incremental Learning Approach for Multi-Scenario Recommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining. 939--948.
[25]
Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng, and Aixin Sun. 2020. CATN: Cross-domain recommendation for cold-start users via aspect transfer network. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 229--238.
[26]
Chenxu Zhu, Peng Du, Xianghui Zhu, Weinan Zhang, Yong Yu, and Yang Cao. 2022. User-tag profile modeling in recommendation system via contrast weighted tag masking. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4630--4638.
[27]
Feng Zhu, Chaochao Chen, Yan Wang, Guanfeng Liu, and Xiaolin Zheng. 2019. Dtcdr: A framework for dual-target cross-domain recommendation. In CIKM. 1533--1542.
[28]
Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, Mehmet Orgun, and Jia Wu. 2020. A deep framework for cross-domain and cross-system recommendations. arXiv preprint arXiv:2009.06215 (2020).
[29]
Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, and Guanfeng Liu. 2021. Cross-domain recommendation: challenges, progress, and prospects. arXiv preprint arXiv:2103.01696 (2021).
[30]
Xianghui Zhu, Peng Du, Shuo Shao, Chenxu Zhu, Weinan Zhang, Yang Wang, and Yang Cao. 2023. A Feature-Based Coalition Game Framework with Privileged Knowledge Transfer for User-tag Profile Modeling. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 5739--5749.
Index Terms
- Modeling Domains as Distributions with Uncertainty for Cross-Domain Recommendation
Recommendations
Domain ranking for cross domain collaborative filtering
UMAP'12: Proceedings of the 20th international conference on User Modeling, Adaptation, and PersonalizationIn recommendation systems a variation of the cold start problem is a situation where the target user has few-to-none item ratings belonging to the target domain (e.g., movies) to base recommendations on. One way to overcome this is by basing ...
Personalized Transfer of User Preferences for Cross-domain Recommendation
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data MiningCold-start problem is still a very challenging problem in recommender systems. Fortunately, the interactions of the cold-start users in the auxiliary source domain can help cold-start recommendations in the target domain. How to transfer user's ...
A Cross-Domain Recommendation Mechanism for Cold-Start Users Based on Partial Least Squares Regression
Regular PapersRecommender systems are common in e-commerce platforms in recent years. Recommender systems are able to help users find preferential items among a large amount of products so that users’ time is saved and sellers’ profits are increased. Cross-domain ...
Comments
Information & Contributors
Information
Published In
July 2024
3164 pages
ISBN:9798400704314
DOI:10.1145/3626772
- General Chairs:
- Grace Hui Yang,
- Hongning Wang,
- Sam Han,
- Program Chairs:
- Claudia Hauff,
- Guido Zuccon,
- Yi Zhang
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 July 2024
Check for updates
Author Tags
Qualifiers
- Short-paper
Funding Sources
- The Special Projects in Key Fields from the Department of Education of Guangdong Province
- Guangdong Provincial Key R&D Program
- Dongguan Science and Technology of Social Development Program
Conference
SIGIR 2024
Sponsor:
SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 14 - 18, 2024
Washington DC, USA
Acceptance Rates
Overall Acceptance Rate 792 of 3,983 submissions, 20%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 577Total Downloads
- Downloads (Last 12 months)577
- Downloads (Last 6 weeks)98
Reflects downloads up to 28 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in