Xiangyu Zhao

Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

2018

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process …

CBR: Context Bias aware Recommendation for Debiasing User Modeling and Click Prediction

2022 · Proceedings of the ACM Web Conference 2022

With the prosperity of recommender systems, the biases existing in user behaviors, which may lead to inconsistency between user preference and behavior records, have attracted wide attention. Though large efforts have been made to infer …

Diffusion Augmentation for Sequential Recommendation

2023

Sequential recommendation (SRS) has become the technical foundation in many applications recently, which aims to recommend the next item based on the user's historical interactions. However, sequential recommendation often faces the problem of data sparsity, …

LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations

2024 · arXiv (Cornell University)

The lack of training data gives rise to the system cold-start problem in recommendation systems, making them struggle to provide effective recommendations. To address this problem, Large Language Models (LLMs) can model recommendation tasks as …

SampleLLM: Optimizing Tabular Data Synthesis in Recommendations

2025

Tabular data synthesis is crucial in machine learning, yet existing general methods-primarily based on statistical or deep learning models-are highly data-dependent and often fall short in recommender systems. This limitation arises from their difficulty in …

Stepwise Reasoning Disruption Attack of LLMs

2025

Jingyu Peng, Maolin Wang, Xiangyu Zhao, Kai Zhang, Wanyu Wang, Pengyue Jia, Qidong Liu, Ruocheng Guo, Qi Liu. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Navigate the Unknown: Enhancing LLM Reasoning with Intrinsic Motivation Guided Exploration

2025 · arXiv (Cornell University)

Reinforcement Learning (RL) has become a key approach for enhancing the reasoning capabilities of large language models. However, prevalent RL approaches like proximal policy optimization and group relative policy optimization suffer from sparse, outcome-based rewards …

Deep Reinforcement Learning for List-wise Recommendations

2017 · arXiv (Cornell University)

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process …

Deep reinforcement learning for page-wise recommendations

2018

Recommender systems can mitigate the information overload problem by suggesting users' personalized items. In real-world recommendations such as e-commerce, a typical interaction between the system and its users is - users are recommended a page …

"Deep reinforcement learning for search, recommendation, and online advertising: a survey" by Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin with Martin Vesely as coordinator

2019 · ACM SIGWEB Newsletter

Search, recommendation, and online advertising are the three most important information-providing mechanisms on the web. These information seeking techniques, satisfying users' information needs by suggesting users personalized objects (information or services) at the appropriate time …

Jointly Learning to Recommend and Advertise

2020

Online recommendation and advertising are two major income channels for online recommendation platforms (e.g. e-commerce and news feed site). However, most platforms optimize recommending and advertising strategies by different teams separately via different techniques, which …

Towards Long-term Fairness in Recommendation

2021

As Recommender Systems (RS) influence more and more people in their daily life, the issue of fairness in recommendation is becoming more and more important. Most of the prior approaches to fairness-aware recommendation have been …

Large language models for generative information extraction: a survey

2024 · Frontiers of Computer Science

Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural language texts. Recently, generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. As a result, numerous works have …