Yuying Zhao
ABSTRACT
Recommender systems (RSs) have gained widespread applications across various domains owing to the superior ability to capture users' interests. However, the complexity and nuanced nature of users' interests, which span a wide range of diversity, pose a significant challenge in delivering fair recommendations. In practice, user preferences vary significantly; some users show a clear preference toward certain item categories, while others have a broad interest in diverse ones. Even though it is expected that all users should receive high-quality recommendations, the effectiveness of RSs in catering to this disparate interest diversity remains under-explored.
In this work, we investigate whether users with varied levels of interest diversity are treated fairly. Our empirical experiments reveal an inherent disparity: users with broader interests often receive lower-quality recommendations. To mitigate this, we propose a multi-interest framework that uses multiple (virtual) interest embeddings rather than single ones to represent users. Specifically, the framework consists of stacked multi-interest representation layers, which include an interest embedding generator that derives virtual interests from shared parameters, and a center embedding aggregator that facilitates multi-hop aggregation. Experiments demonstrate the effectiveness of the framework in achieving better trade-off between fairness and utility across various datasets and backbones.
Supplemental Material
- Charu C Aggarwal. 2016. Evaluating recommender systems. Recommender Systems: The Textbook (2016), 225--254.Google Scholar
Digital Library
- Yukuo Cen, Jianwei Zhang, Xu Zou, Chang Zhou, Hongxia Yang, and Jie Tang. 2020. Controllable multi-interest framework for recommendation. In Proceedings of KDD. 2942--2951.Google ScholarDigital Library
- Huiyuan Chen, Lan Wang, Yusan Lin, Chin-Chia Michael Yeh, Fei Wang, and Hao Yang. 2021. Structured graph convolutional networks with stochastic masks for recommender systems. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.Google ScholarDigital Library
- Huiyuan Chen, Chin-Chia Michael Yeh, Fei Wang, and Hao Yang. 2022. Graph neural transport networks with non-local attentions for recommender systems. In Proceedings of the ACM Web Conference 2022.Google ScholarDigital Library
- Gautam Choudhary, Iftikhar Ahamath Burhanuddin, Eunyee Koh, Fan Du, and Ryan A Rossi. 2022. PersonaSAGE: A Multi-Persona Graph Neural Network. arXiv preprint arXiv:2212.13709 (2022).Google Scholar
- Konstantina Christakopoulou, Alberto Lalama, Cj Adams, Iris Qu, Yifat Amir, Samer Chucri, Pierce Vollucci, Fabio Soldo, Dina Bseiso, Sarah Scodel, et al. 2023. Large Language Models for User Interest Journeys. arXiv preprint arXiv:2305.15498 (2023).Google Scholar
- Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogin, and Tommaso Di Noia. 2021. A flexible framework for evaluating user and item fairness in recommender systems. User Modeling and User-Adapted Interaction (2021), 1--55.Google Scholar
- Alessandro Epasto and Bryan Perozzi. 2019. Is a single embedding enough? learning node representations that capture multiple social contexts. In The world wide web conference. 394--404.Google Scholar
- Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph neural networks for social recommendation. In The world wide web conference. 417--426.Google Scholar
- Golnoosh Farnadi, Pigi Kouki, Spencer K Thompson, Sriram Srinivasan, and Lise Getoor. 2018. A fairness-aware hybrid recommender system. arXiv preprint arXiv:1809.09030 (2018).Google Scholar
- Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, et al. 2020. Fairness-aware explainable recommendation over knowledge graphs. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 69--78.Google ScholarDigital Library
- Sruthi Gorantla, Amit Deshpande, and Anand Louis. 2021. On the problem of underranking in group-fair ranking. In International Conference on Machine Learning. PMLR, 3777--3787.Google Scholar
- Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. 2018. Fairness without demographics in repeated loss minimization. In International Conference on Machine Learning. PMLR, 1929--1938.Google Scholar
- 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.Google ScholarDigital Library
- Hongye Jin, Xiaotian Han, Jingfeng Yang, Zhimeng Jiang, Zirui Liu, Chia-Yuan Chang, Huiyuan Chen, and Xia Hu. 2024. LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning. arXiv preprint arXiv:2401.01325 (2024).Google Scholar
- Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed Chi. 2020. Fairness without demographics through adversarially reweighted learning. Advances in neural information processing systems, Vol. 33 (2020), 728--740.Google Scholar
- Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Huan Zhao, Pipei Huang, Guoliang Kang, Qiwei Chen, Wei Li, and Dik Lun Lee. 2019. Multi-interest network with dynamic routing for recommendation at Tmall. In Proceedings of the 28th ACM international conference on information and knowledge management. 2615--2623.Google ScholarDigital Library
- Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2021. User-oriented fairness in recommendation. In Proceedings of the Web Conference 2021. 624--632.Google ScholarDigital Library
- Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, and Yongfeng Zhang. 2022. Fairness in recommendation: A survey. arXiv preprint arXiv:2205.13619 (2022).Google Scholar
- Jiahui Liu, Peter Dolan, and Elin Rønby Pedersen. 2010. Personalized news recommendation based on click behavior. In Proceedings of the 15th international conference on Intelligent user interfaces. 31--40.Google ScholarDigital Library
- James MacQueen et al. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Vol. 1. Oakland, CA, USA, 281--297.Google Scholar
- Aditya Pal, Chantat Eksombatchai, Yitong Zhou, Bo Zhao, Charles Rosenberg, and Jure Leskovec. 2020. Pinnersage: Multi-modal user embedding framework for recommendations at pinterest. In SIGKDD. 2311--2320.Google ScholarDigital Library
- Chanyoung Park, Carl Yang, Qi Zhu, Donghyun Kim, Hwanjo Yu, and Jiawei Han. 2020. Unsupervised differentiable multi-aspect network embedding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1435--1445.Google ScholarDigital Library
- Hossein A Rahmani, Yashar Deldjoo, Ali Tourani, and Mohammadmehdi Naghiaei. 2022. The unfairness of active users and popularity bias in point-of-interest recommendation. In International Workshop on Algorithmic Bias in Search and Recommendation. Springer, 56--68.Google ScholarCross Ref
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012).Google Scholar
- Hui Shi, Yupeng Gu, Yitong Zhou, Bo Zhao, Sicun Gao, and Jishen Zhao. 2022. Every Preference Changes Differently: Neural Multi-Interest Preference Model with Temporal Dynamics for Recommendation. arXiv (2022).Google Scholar
- Edward H Simpson. 1949. Measurement of diversity. nature, Vol. 163, 4148 (1949), 688--688.Google Scholar
- Robert L Thorndike. 1953. Who belongs in the family? Psychometrika, Vol. 18, 4 (1953), 267--276.Google ScholarCross Ref
- Chenyang Wang, Yuanqing Yu, Weizhi Ma, Min Zhang, Chong Chen, Yiqun Liu, and Shaoping Ma. 2022a. Towards representation alignment and uniformity in collaborative filtering. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1816--1825.Google ScholarDigital Library
- Tongzhou Wang and Phillip Isola. 2020. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In International Conference on Machine Learning. PMLR, 9929--9939.Google Scholar
- Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, and Shaoping Ma. 2023 a. A survey on the fairness of recommender systems. ACM Transactions on Information Systems, Vol. 41, 3 (2023), 1--43.Google ScholarDigital Library
- Yu Wang, Yuying Zhao, Yushun Dong, Huiyuan Chen, Jundong Li, and Tyler Derr. 2022b. Improving fairness in graph neural networks via mitigating sensitive attribute leakage. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.Google ScholarDigital Library
- Yu Wang, Yuying Zhao, Yi Zhang, and Tyler Derr. 2023 b. Collaboration-Aware Graph Convolutional Network for Recommender Systems. In Proceedings of the ACM Web Conference 2023. 91--101.Google ScholarDigital Library
- Joe H Ward Jr. 1963. Hierarchical grouping to optimize an objective function. Journal of the American statistical association, Vol. 58, 301 (1963), 236--244.Google ScholarCross Ref
- Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang. 2022. Are Big Recommendation Models Fair to Cold Users? arXiv preprint arXiv:2202.13607 (2022).Google Scholar
- Chuhan Wu, Fangzhao Wu, Xiting Wang, Yongfeng Huang, and Xing Xie. 2021. Fairness-aware news recommendation with decomposed adversarial learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 4462--4469.Google ScholarCross Ref
- Yuchen Yan, Yuzhong Chen, Huiyuan Chen, Minghua Xu, Mahashweta Das, Hao Yang, and Hanghang Tong. 2023 a. From Trainable Negative Depth to Edge Heterophily in Graphs. In Thirty-seventh Conference on Neural Information Processing Systems.Google Scholar
- Yuchen Yan, Baoyu Jing, Lihui Liu, Ruijie Wang, Jinning Li, Tarek Abdelzaher, and Hanghang Tong. 2023 b. Reconciling Competing Sampling Strategies of Network Embedding. In Thirty-seventh Conference on Neural Information Processing Systems.Google Scholar
- Yuchen Yan, Si Zhang, and Hanghang Tong. 2021. Bright: A bridging algorithm for network alignment. In Proceedings of the Web Conference 2021.Google ScholarDigital Library
- Yuchen Yan, Qinghai Zhou, Jinning Li, Tarek Abdelzaher, and Hanghang Tong. 2022. Dissecting cross-layer dependency inference on multi-layered inter-dependent networks. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management.Google ScholarDigital Library
- Shengyu Zhang, Lingxiao Yang, Dong Yao, Yujie Lu, Fuli Feng, Zhou Zhao, Tat-Seng Chua, and Fei Wu. 2022. Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation. In WebConf. 2216--2226.Google Scholar
- Yuying Zhao, Yu Wang, Yunchao Liu, Xueqi Cheng, Charu Aggarwal, and Tyler Derr. 2023. Fairness and Diversity in Recommender Systems: A Survey. arXiv preprint arXiv:2307.04644 (2023).Google Scholar
- Yuying Zhao, Yu Wang, Yi Zhang, Pamela Wisniewski, Charu Aggarwal, and Tyler Derr. 2024. Leveraging Opposite Gender Interaction Ratio as a Path towards Fairness in Online Dating Recommendations Based on User Sexual Orientation. In Proceedings of the AAAI Conference on Artificial Intelligence.Google ScholarCross Ref
- Ziwei Zhu, Xia Hu, and James Caverlee. 2018. Fairness-aware tensor-based recommendation. In Proceedings of the 27th ACM international conference on information and knowledge management. 1153--1162.Google ScholarDigital Library
Index Terms
- Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness
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