25 citations · 27 across the 10 of their papers we have counts for
10 papers
Reasoning While Recommending: Entropy-Guided Latent Reasoning in Generative Re-ranking Models
Changshuo Zhang
Reinforcement learning plays a crucial role in generative re-ranking scenarios due to its exploration-exploitation capabilities, but existing generative methods mostly fail to adap…
Process In-Context Learning: Enhancing Mathematical Reasoning via Dynamic Demonstration Insertion
Ang Gao, Changshuo Zhang, Xiao Zhang +4
In-context learning (ICL) has proven highly effective across diverse large language model (LLM) tasks. However, its potential for enhancing tasks that demand step-by-step logical d…
Comment Staytime Prediction with LLM-enhanced Comment Understanding
Changshuo Zhang, Zihan Lin, Shukai Liu +2
In modern online streaming platforms, the comments section plays a critical role in enhancing the overall user experience. Understanding user behavior within the comments section i…
Test-Time Alignment for Tracking User Interest Shifts in Sequential Recommendation
Changshuo Zhang, Xiao Zhang, Teng Shi +2
Sequential recommendation is essential in modern recommender systems, aiming to predict the next item a user may interact with based on their historical behaviors. However, real-wo…
Modeling Domain and Feedback Transitions for Cross-Domain Sequential Recommendation
Changshuo Zhang, Teng Shi, Xiao Zhang +4
Nowadays, many recommender systems encompass various domains to cater to users' diverse needs, leading to user behaviors transitioning across different domains. In fact, user behav…
A Survey of Controllable Learning: Methods and Applications in Information Retrieval
Chenglei Shen, Xiao Zhang, Teng Shi +3
Controllability has become a crucial aspect of trustworthy machine learning, enabling learners to meet predefined targets and adapt dynamically at test time without requiring retra…
Do Not Wait: Learning Re-Ranking Model Without User Feedback At Serving Time in E-Commerce
Yuan Wang, Zhiyu Li, Changshuo Zhang +4
Recommender systems have been widely used in e-commerce, and re-ranking models are playing an increasingly significant role in the domain, which leverages the inter-item influence…
QAGCF: Graph Collaborative Filtering for Q&A Recommendation
Changshuo Zhang, Teng Shi, Xiao Zhang +5
Question and answer (Q&A) platforms usually recommend question-answer pairs to meet users' knowledge acquisition needs, unlike traditional recommendations that recommend only one i…
UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systems
Changshuo Zhang, Sirui Chen, Xiao Zhang +3
Reinforcement learning (RL) has gained traction for enhancing user long-term experiences in recommender systems by effectively exploring users' interests. However, modern recommend…
Controllable Multi-Objective Re-ranking with Policy Hypernetworks
Sirui Chen, Yuan Wang, Zijing Wen +6
Multi-stage ranking pipelines have become widely used strategies in modern recommender systems, where the final stage aims to return a ranked list of items that balances a number o…