no code implementations • 25 Jan 2023 • Yunqi Li, Dingxian Wang, Hanxiong Chen, Yongfeng Zhang
The proposed method is able to transfer the knowledge of a fair model learned from the source users to the target users with the hope of improving the recommendation performance and keeping the fairness property on the target users.
1 code implementation • 23 Aug 2022 • Hanxiong Chen, Yunqi Li, He Zhu, Yongfeng Zhang
Experiments on different datasets show that the adaptive architecture assembled by MANAS outperforms static global architectures.
no code implementations • 26 May 2022 • Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, Yongfeng Zhang
It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking in order to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems.
no code implementations • 27 Dec 2021 • Hanxiong Chen, Yunqi Li, Shaoyun Shi, Shuchang Liu, He Zhu, Yongfeng Zhang
Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering.
1 code implementation • 20 May 2021 • Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Yongfeng Zhang
Therefore, it is important to provide personalized fair recommendations for users to satisfy their personalized fairness demands.
1 code implementation • 21 Apr 2021 • Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, Yongfeng Zhang
To solve this problem, we provide a re-ranking approach to mitigate this unfairness problem by adding constraints over evaluation metrics.
no code implementations • 9 Jan 2021 • Hanxiong Chen, Xu Chen, Shaoyun Shi, Yongfeng Zhang
Motivated by this problem, we propose to generate free-text natural language explanations for personalized recommendation.
3 code implementations • 20 Aug 2020 • Shaoyun Shi, Hanxiong Chen, Weizhi Ma, Jiaxin Mao, Min Zhang, Yongfeng Zhang
Both reasoning and generalization ability are important for prediction tasks such as recommender systems, where reasoning provides strong connection between user history and target items for accurate prediction, and generalization helps the model to draw a robust user portrait over noisy inputs.
3 code implementations • 16 May 2020 • Hanxiong Chen, Shaoyun Shi, Yunqi Li, Yongfeng Zhang
Existing Collaborative Filtering (CF) methods are mostly designed based on the idea of matching, i. e., by learning user and item embeddings from data using shallow or deep models, they try to capture the associative relevance patterns in data, so that a user embedding can be matched with relevant item embeddings using designed or learned similarity functions.
no code implementations • 17 Oct 2019 • Shaoyun Shi, Hanxiong Chen, Min Zhang, Yongfeng Zhang
The fundamental idea behind the design of most neural networks is to learn similarity patterns from data for prediction and inference, which lacks the ability of logical reasoning.