Search Results for author: Hanxiong Chen

Found 6 papers, 3 papers with code

Personalized Counterfactual Fairness in Recommendation

no code implementations20 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.

Decision Making Fairness +2

User-oriented Fairness in Recommendation

1 code implementation21 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.

Fairness Recommendation Systems +1

Generate Natural Language Explanations for Recommendation

no code implementations9 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.

Denoising Recommendation Systems

Neural Logic Reasoning

1 code implementation20 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.

Recommendation Systems

Neural Collaborative Reasoning

3 code implementations16 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.

Collaborative Filtering Decision Making +2

Neural Logic Networks

no code implementations17 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.

Collaborative Filtering

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