187 citations · 345 across the 14 of their papers we have counts for
14 papers
Generative Retrieval with Semantic Tree-Structured Item Identifiers via Contrastive Learning
Zihua Si, Zhongxiang Sun, Jiale Chen +7
The retrieval phase is a vital component in recommendation systems, requiring the model to be effective and efficient. Recently, generative retrieval has become an emerging paradig…
HyperBandit: Contextual Bandit with Hypernewtork for Time-Varying User Preferences in Streaming Recommendation
Chenglei Shen, Xiao Zhang, Wei Wei +1
In real-world streaming recommender systems, user preferences often dynamically change over time (e.g., a user may have different preferences during weekdays and weekends). Existin…
LTP-MMF: Towards Long-term Provider Max-min Fairness Under Recommendation Feedback Loops
Chen Xu, Xiaopeng Ye, Jun Xu +3
Multi-stakeholder recommender systems involve various roles, such as users, and providers. Previous work pointed out that max-min fairness (MMF) is a better metric to support weak…
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…
KuaiSAR: A Unified Search And Recommendation Dataset
Zhongxiang Sun, Zihua Si, Xiaoxue Zang +5
The confluence of Search and Recommendation (S&R) services is vital to online services, including e-commerce and video platforms. The integration of S&R modeling is a highly intuit…
When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation
Zihua Si, Zhongxiang Sun, Xiao Zhang +5
Modern online service providers such as online shopping platforms often provide both search and recommendation (S&R) services to meet different user needs. Rarely has there been an…
Uncovering ChatGPT's Capabilities in Recommender Systems
Sunhao Dai, Ninglu Shao, Haiyuan Zhao +6
The debut of ChatGPT has recently attracted the attention of the natural language processing (NLP) community and beyond. Existing studies have demonstrated that ChatGPT shows signi…
P-MMF: Provider Max-min Fairness Re-ranking in Recommender System
Chen Xu, Sirui Chen, Jun Xu +4
In this paper, we address the issue of recommending fairly from the aspect of providers, which has become increasingly essential in multistakeholder recommender systems. Existing s…
Law Article-Enhanced Legal Case Matching: a Causal Learning Approach
Zhongxiang Sun, Jun Xu, Xiao Zhang +2
Legal case matching, which automatically constructs a model to estimate the similarities between the source and target cases, has played an essential role in intelligent legal syst…
Reward Imputation with Sketching for Contextual Batched Bandits
Xiao Zhang, Ninglu Shao, Zihua Si +4
Contextual batched bandit (CBB) is a setting where a batch of rewards is observed from the environment at the end of each episode, but the rewards of the non-executed actions are u…
Reinforcement Re-ranking with 2D Grid-based Recommendation Panels
Sirui Chen, Xiao Zhang, Xu Chen +4
Modern recommender systems usually present items as a streaming, one-dimensional ranking list. Recently there is a trend in e-commerce that the recommended items are organized grid…
Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition
Haiyuan Zhao, Jun Xu, Xiao Zhang +3
Unbiased learning to rank has been proposed to alleviate the biases in the search ranking, making it possible to train ranking models with user interaction data. In real applicatio…
A Model-Agnostic Causal Learning Framework for Recommendation using Search Data
Zihua Si, Xueran Han, Xiao Zhang +4
Machine-learning based recommender systems(RSs) has become an effective means to help people automatically discover their interests. Existing models often represent the rich inform…
Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient
Haonan Jia, Xiao Zhang, Jun Xu +4
Deep Q-learning algorithms often suffer from poor gradient estimations with an excessive variance, resulting in unstable training and poor sampling efficiency. Stochastic variance-…