Search Results for author: Chongming Gao

Found 16 papers, 9 papers with code

Treatment Effect Estimation for User Interest Exploration on Recommender Systems

1 code implementation14 May 2024 Jiaju Chen, Wenjie Wang, Chongming Gao, Peng Wu, Jianxiong Wei, Qingsong Hua

The empirical results validate the effectiveness of UpliftRec in discovering users' hidden interests while achieving superior recommendation accuracy.

Recommendation Systems Scheduling

How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective

no code implementations18 Apr 2024 Siyi Lin, Chongming Gao, Jiawei Chen, Sheng Zhou, Binbin Hu, Chun Chen, Can Wang

Building on these insights, we propose a novel debiasing strategy that leverages a spectral norm regularizer to penalize the magnitude of the principal singular value.

Fairness Recommendation Systems

Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients

no code implementations26 Mar 2024 Zihao Zhao, Yi Jing, Fuli Feng, Jiancan Wu, Chongming Gao, Xiangnan He

Medication recommendation systems have gained significant attention in healthcare as a means of providing tailored and effective drug combinations based on patients' clinical information.

Fairness Recommendation Systems

Large Language Models are Learnable Planners for Long-Term Recommendation

1 code implementation29 Feb 2024 Wentao Shi, Xiangnan He, Yang Zhang, Chongming Gao, Xinyue Li, Jizhi Zhang, Qifan Wang, Fuli Feng

To this end, we propose a Bi-level Learnable LLM Planner framework, which consists of a set of LLM instances and breaks down the learning process into macro-learning and micro-learning to learn macro-level guidance and micro-level personalized recommendation policies, respectively.

Decision Making Language Modelling +2

RecAD: Towards A Unified Library for Recommender Attack and Defense

1 code implementation9 Sep 2023 Changsheng Wang, Jianbai Ye, Wenjie Wang, Chongming Gao, Fuli Feng, Xiangnan He

Despite significant research progress in recommender attack and defense, there is a lack of a widely-recognized benchmarking standard in the field, leading to unfair performance comparison and limited credibility of experiments.

Benchmarking Recommendation Systems

Adaptive Vague Preference Policy Learning for Multi-round Conversational Recommendation

no code implementations7 Jun 2023 Gangyi Zhang, Chongming Gao, Wenqiang Lei, Xiaojie Guo, Shijun Li, Hongshen Chen, Zhuozhi Ding, Sulong Xu, Lingfei Wu

In the VPMCR setting, we propose a solution called Adaptive Vague Preference Policy Learning (AVPPL), which consists of two components: Ambiguity-aware Soft Estimation (ASE) and Dynamism-aware Policy Learning (DPL).

Decision Making Recommendation Systems

On the Theories Behind Hard Negative Sampling for Recommendation

1 code implementation7 Feb 2023 Wentao Shi, Jiawei Chen, Fuli Feng, Jizhi Zhang, Junkang Wu, Chongming Gao, Xiangnan He

Secondly, we prove that OPAUC has a stronger connection with Top-K evaluation metrics than AUC and verify it with simulation experiments.

Recommendation Systems

CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System

1 code implementation4 Apr 2022 Chongming Gao, Shiqi Wang, Shijun Li, Jiawei Chen, Xiangnan He, Wenqiang Lei, Biao Li, Yuan Zhang, Peng Jiang

The basic idea is to first learn a causal user model on historical data to capture the overexposure effect of items on user satisfaction.

Causal Inference counterfactual +2

KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems

3 code implementations22 Feb 2022 Chongming Gao, Shijun Li, Wenqiang Lei, Jiawei Chen, Biao Li, Peng Jiang, Xiangnan He, Jiaxin Mao, Tat-Seng Chua

The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive.

Recommendation Systems User Simulation

Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion

no code implementations19 Feb 2022 Shiqi Wang, Chongming Gao, Min Gao, Junliang Yu, Zongwei Wang, Hongzhi Yin

By providing users with opportunities to experience goods without charge, a free trial makes adopters know more about products and thus encourages their willingness to buy.

Marketing reinforcement-learning +1

Generating Reliable Friends via Adversarial Training to Improve Social Recommendation

no code implementations8 Sep 2019 Junliang Yu, Min Gao, Hongzhi Yin, Jundong Li, Chongming Gao, Qinyong Wang

Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems.

Recommendation Systems

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