1 code implementation • 22 May 2023 • Yunqi Li, Yongfeng Zhang
Understanding and addressing unfairness in LLMs are crucial for responsible AI deployment.
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.
no code implementations • 8 Jan 2023 • Shuyuan Xu, Jianchao Ji, Yunqi Li, Yingqiang Ge, Juntao Tan, Yongfeng Zhang
We discuss the fundamental concepts of both recommender systems and causal inference as well as their relationship, and review the existing work on causal methods for different problems in recommender systems.
no code implementations • 25 Nov 2022 • Bernie Boscoe, Tuan Do, Evan Jones, Yunqi Li, Kevin Alfaro, Christy Ma
We define effective machine learning datasets in astronomy to be formed with well-defined data points, structure, and metadata.
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 • 25 Jul 2022 • Yingqiang Ge, Shuchang Liu, Zuohui Fu, Juntao Tan, Zelong Li, Shuyuan Xu, Yunqi Li, Yikun Xian, Yongfeng Zhang
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process.
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.
1 code implementation • 17 Feb 2022 • Juntao Tan, Shijie Geng, Zuohui Fu, Yingqiang Ge, Shuyuan Xu, Yunqi Li, Yongfeng Zhang
For quantitatively evaluating the generated explanations without the requirement of ground-truth, we design metrics based on Counterfactual and Factual reasoning to evaluate the necessity and sufficiency of the explanations.
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.
no code implementations • 5 Sep 2021 • Yingqiang Ge, Shuchang Liu, Zelong Li, Shuyuan Xu, Shijie Geng, Yunqi Li, Juntao Tan, Fei Sun, Yongfeng Zhang
While recent years have witnessed the emergence of various explainable methods in machine learning, to what degree the explanations really represent the reasoning process behind the model prediction -- namely, the faithfulness of explanation -- is still an open problem.
2 code implementations • 24 Aug 2021 • Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, Yongfeng Zhang
Technically, for each item recommended to each user, CountER formulates a joint optimization problem to generate minimal changes on the item aspects so as to create a counterfactual item, such that the recommendation decision on the counterfactual item is reversed.
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.
1 code implementation • 3 Feb 2021 • Shuyuan Xu, Yingqiang Ge, Yunqi Li, Zuohui Fu, Xu Chen, Yongfeng Zhang
However, pure correlative learning may lead to Simpson's paradox in predictions, and thus results in sacrificed recommendation performance.
no code implementations • 13 Jan 2021 • Yunqi Li, Shuyuan Xu, Bo Liu, Zuohui Fu, Shuchang Liu, Xu Chen, Yongfeng Zhang
This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable discrete optimization algorithm, to solve the formidable storage and computation cost challenges in traditional continuous graph embedding methods.
1 code implementation • 10 Jan 2021 • Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, Yongfeng Zhang
We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process.
no code implementations • 30 Jun 2020 • Shuyuan Xu, Yunqi Li, Shuchang Liu, Zuohui Fu, Xu Chen, Yongfeng Zhang
State-of-the-art recommender systems have the ability to generate high-quality recommendations, but usually cannot provide intuitive explanations to humans due to the usage of black-box prediction models.
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.
5 code implementations • 10 Apr 2019 • Philip Daian, Steven Goldfeder, Tyler Kell, Yunqi Li, Xueyuan Zhao, Iddo Bentov, Lorenz Breidenbach, Ari Juels
We study the breadth of DEX arbitrage bots in a subset of transactions that yield quantifiable revenue to these bots.
Cryptography and Security Computer Science and Game Theory