skip to main content
10.1145/3664647.3681692acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
research-article

Tag Tree-Guided Multi-grained Alignment for Multi-Domain Short Video Recommendation

Published: 28 October 2024 Publication History

Abstract

Multi-Domain Recommendation (MDR) aims to leverage data from multiple domains to enhance recommendations through overlapping users or items. However, extreme overlap sparsity in some applications makes it challenging for existing multi-domain models to capture domain-shared information. Also, the sparse overlapping users or items result in a cold start problem in every single domain and hinder feature space alignment of different domains, posing a challenge for joint optimization across domains. However, in multi-domain short video recommendation, we identify two key characteristics that can greatly alleviate the overlapping sparsity issue and enable domain alignment. (1) The following relations between users and publishers exhibit strong preferences and a concentration effect, as popular video publishers, who constitute a small portion of all users, are followed by a majority of users across various domains. (2) The tag tree structure shared by all videos can help facilitate multi-grained alignment across multiple domains. Based on these characteristics, we propose tag tree-guided multi-grained alignment with publisher enhancement for multi-domain video recommendation. Our model integrates publisher and tag nodes into the user-video bipartite graph as central nodes, enabling user and video alignment across all domains via graph propagation. Then, we propose a tag tree-guided decomposition method to obtain hierarchical graphs for multi-grained alignment. Further, we design tree-guided contrastive learning methods to capture the intra-level and inter-level node relations respectively. Finally, extensive experiments on two real-world short video recommendation datasets demonstrate the effectiveness of our model. Our code is available at https://github.com/17231087/TGMAPE.git

References

[1]
Jianxin Chang, Chenbin Zhang, Yiqun Hui, Dewei Leng, Yanan Niu, Yang Song, and Kun Gai. 2023. PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3795--3804.
[2]
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.
[3]
Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems. 191--198.
[4]
Cesar Ferri, José Hernández-Orallo, and Peter A Flach. 2011. A coherent interpretation of AUC as a measure of aggregated classification performance. In Proceedings of the 28th International Conference on Machine Learning (ICML-11). 657--664.
[5]
Xudong Gong, Qinlin Feng, Yuan Zhang, Jiangling Qin, Weijie Ding, Biao Li, Peng Jiang, and Kun Gai. 2022. Real-time short video recommendation on mobile devices. In Proceedings of the 31st ACM international conference on information & knowledge management. 3103--3112.
[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]
Xiaobo Hao, Yudan Liu, Ruobing Xie, Kaikai Ge, Linyao Tang, Xu Zhang, and Leyu Lin. 2021. Adversarial feature translation for multi-domain recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2964--2973.
[8]
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. 639--648.
[9]
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.
[10]
Thomas N Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations.
[11]
Jiacheng Li, Yujie Wang, and Julian McAuley. 2020. Time interval aware self-attention for sequential recommendation. In Proceedings of the 13th international conference on web search and data mining. 322--330.
[12]
Pengcheng Li, Runze Li, Qing Da, An-Xiang Zeng, and Lijun Zhang. 2020. Improving multi-scenario learning to rank in e-commerce by exploiting task relationships in the label space. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2605--2612.
[13]
Xiaopeng Li, Fan Yan, Xiangyu Zhao, Yichao Wang, Bo Chen, Huifeng Guo, and Ruiming Tang. 2023. Hamur: Hyper adapter for multi-domain recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 1268--1277.
[14]
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018. Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1930--1939.
[15]
Wentao Ning, Xiao Yan, Weiwen Liu, Reynold Cheng, Rui Zhang, and Bo Tang. 2023. Multi-domain Recommendation with Embedding Disentangling and Domain Alignment. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 1917--1927.
[16]
Xichuan Niu, Bofang Li, Chenliang Li, Jun Tan, Rong Xiao, and Hongbo Deng. 2021. Heterogeneous graph augmented multi-scenario sharing recommendation with tree-guided expert networks. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 1038--1046.
[17]
Xubin Ren, Lianghao Xia, Jiashu Zhao, Dawei Yin, and Chao Huang. 2023. Disentangled Contrastive Collaborative Filtering. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (, Taipei, Taiwan,) (SIGIR '23). Association for Computing Machinery, New York, NY, USA, 1137--1146. https://doi.org/10.1145/3539618.3591665
[18]
Qijie Shen, Wanjie Tao, Jing Zhang, Hong Wen, Zulong Chen, and Quan Lu. 2021. Sar-net: a scenario-aware ranking network for personalized fair recommendation in hundreds of travel scenarios. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 4094--4103.
[19]
Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou, Xinyao Ding, Binding Dai, Qiang Luo, Siran Yang, Jingshan Lv, Chi Zhang, Hongbo Deng, et al. 2021. One model to serve all: Star topology adaptive recommender for multi-domain ctr prediction. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 4104--4113.
[20]
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management. 1441--1450.
[21]
Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong. 2020. Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations. In Proceedings of the 14th ACM Conference on Recommender Systems. 269--278.
[22]
Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019. Kgat: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 950--958.
[23]
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval. 165--174.
[24]
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 Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4090--4099.
[25]
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In International conference on machine learning. PMLR, 6861--6871.
[26]
Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui. 2022. Graph neural networks in recommender systems: a survey. Comput. Surveys, Vol. 55, 5 (2022), 1--37.
[27]
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, Vol. 32, 1 (2020), 4--24.
[28]
Xuanhua Yang, Xiaoyu Peng, Penghui Wei, Shaoguo Liu, Liang Wang, and Bo Zheng. 2022. AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 4635--4639.
[29]
Yuhao Yang, Chao Huang, Lianghao Xia, Chunzhen Huang, Da Luo, and Kangyi Lin. 2023. Debiased contrastive learning for sequential recommendation. In Proceedings of the ACM web conference 2023. 1063--1073.
[30]
Yuhao Yang, Chao Huang, Lianghao Xia, Yuxuan Liang, Yanwei Yu, and Chenliang Li. 2022. Multi-behavior hypergraph-enhanced transformer for sequential recommendation. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining. 2263--2274.
[31]
Tianzi Zang, Yanmin Zhu, Haobing Liu, Ruohan Zhang, and Jiadi Yu. 2022. A survey on cross-domain recommendation: taxonomies, methods, and future directions. ACM Transactions on Information Systems, Vol. 41, 2 (2022), 1--39.
[32]
Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1059--1068.
[33]
Jie Zhou, Xianshuai Cao, Wenhao Li, Lin Bo, Kun Zhang, Chuan Luo, and Qian Yu. 2023. HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction. In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 2969--2975.
[34]
Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, and Guanfeng Liu. 2021. Cross-domain recommendation: challenges, progress, and prospects. In 30th International Joint Conference on Artificial Intelligence, IJCAI 2021. International Joint Conferences on Artificial Intelligence, 4721--4728.

Index Terms

  1. Tag Tree-Guided Multi-grained Alignment for Multi-Domain Short Video Recommendation

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    MM '24: Proceedings of the 32nd ACM International Conference on Multimedia
    October 2024
    11719 pages
    ISBN:9798400706868
    DOI:10.1145/3664647
    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

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 28 October 2024

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. multi-domain recommendation
    2. multi-grained domain alignment
    3. popular publisher enhancement
    4. tree structure

    Qualifiers

    • Research-article

    Funding Sources

    Conference

    MM '24
    Sponsor:
    MM '24: The 32nd ACM International Conference on Multimedia
    October 28 - November 1, 2024
    Melbourne VIC, Australia

    Acceptance Rates

    MM '24 Paper Acceptance Rate 1,150 of 4,385 submissions, 26%;
    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 110
      Total Downloads
    • Downloads (Last 12 months)110
    • Downloads (Last 6 weeks)19
    Reflects downloads up to 17 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media