130 citations · 744 across the 49 of their papers we have counts for
30 papers · 1 filter
ID-centric Pre-training for Recommendation
Yiqing Wu, Ruobing Xie, Zhao Zhang +5
Classical sequential recommendation models generally adopt ID embeddings to store knowledge learned from user historical behaviors and represent items. However, these unique IDs ar…
Plug-in Diffusion Model for Sequential Recommendation
Haokai Ma, Ruobing Xie, Lei Meng +4
Pioneering efforts have verified the effectiveness of the diffusion models in exploring the informative uncertainty for recommendation. Considering the difference between recommend…
Curriculum-scheduled Knowledge Distillation from Multiple Pre-trained Teachers for Multi-domain Sequential Recommendation
Wenqi Sun, Ruobing Xie, Junjie Zhang +3
Pre-trained recommendation models (PRMs) have received increasing interest recently. However, their intrinsically heterogeneous model structure, huge model size and computation cos…
AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems
Junjie Zhang, Yupeng Hou, Ruobing Xie +5
Recently, there has been an emergence of employing LLM-powered agents as believable human proxies, based on their remarkable decision-making capability. However, existing studies m…
Learning from All Sides: Diversified Positive Augmentation via Self-distillation in Recommendation
Chong Liu, Xiaoyang Liu, Ruobing Xie +3
Personalized recommendation relies on user historical behaviors to provide user-interested items, and thus seriously struggles with the data sparsity issue. A powerful positive ite…
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach
Junjie Zhang, Ruobing Xie, Yupeng Hou +3
In the past decades, recommender systems have attracted much attention in both research and industry communities, and a large number of studies have been devoted to developing effe…
Attacking Pre-trained Recommendation
Yiqing Wu, Ruobing Xie, Zhao Zhang +5
Recently, a series of pioneer studies have shown the potency of pre-trained models in sequential recommendation, illuminating the path of building an omniscient unified pre-trained…
Large Language Models are Zero-Shot Rankers for Recommender Systems
Yupeng Hou, Junjie Zhang, Zihan Lin +4
Recently, large language models (LLMs) (e.g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks.…
Triple Sequence Learning for Cross-domain Recommendation
Haokai Ma, Ruobing Xie, Lei Meng +4
Cross-domain recommendation (CDR) aims to leverage the correlation of users' behaviors in both the source and target domains to improve the user preference modeling in the target d…
A Survey on Causal Inference for Recommendation
Huishi Luo, Fuzhen Zhuang, Ruobing Xie +4
Causal inference has recently garnered significant interest among recommender system (RS) researchers due to its ability to dissect cause-and-effect relationships and its broad app…
Adversarial Learning Data Augmentation for Graph Contrastive Learning in Recommendation
Junjie Huang, Qi Cao, Ruobing Xie +4
Recently, Graph Neural Networks (GNNs) achieve remarkable success in Recommendation. To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-ba…
Reweighting Clicks with Dwell Time in Recommendation
Ruobing Xie, Lin Ma, Shaoliang Zhang +2
The click behavior is the most widely-used user positive feedback in recommendation. However, simply considering each click equally in training may suffer from clickbaits and title…
Multi-granularity Item-based Contrastive Recommendation
Ruobing Xie, Zhijie Qiu, Bo Zhang +1
Contrastive learning (CL) has shown its power in recommendation. However, most CL-based recommendation models build their CL tasks merely focusing on the user's aspects, ignoring t…
Customized Conversational Recommender Systems
Shuokai Li, Yongchun Zhu, Ruobing Xie +5
Conversational recommender systems (CRS) aim to capture user's current intentions and provide recommendations through real-time multi-turn conversational interactions. As a human-m…
Personalized Prompt for Sequential Recommendation
Yiqing Wu, Ruobing Xie, Yongchun Zhu +4
Pre-training models have shown their power in sequential recommendation. Recently, prompt has been widely explored and verified for tuning in NLP pre-training, which could help to…
Selective Fairness in Recommendation via Prompts
Yiqing Wu, Ruobing Xie, Yongchun Zhu +5
Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and user…
User-Centric Conversational Recommendation with Multi-Aspect User Modeling
Shuokai Li, Ruobing Xie, Yongchun Zhu +3
Conversational recommender systems (CRS) aim to provide highquality recommendations in conversations. However, most conventional CRS models mainly focus on the dialogue understandi…
Multi-view Multi-behavior Contrastive Learning in Recommendation
Yiqing Wu, Ruobing Xie, Yongchun Zhu +6
Multi-behavior recommendation (MBR) aims to jointly consider multiple behaviors to improve the target behavior's performance. We argue that MBR models should: (1) model the coarse-…
Contrastive Cross-domain Recommendation in Matching
Ruobing Xie, Qi Liu, Liangdong Wang +3
Cross-domain recommendation (CDR) aims to provide better recommendation results in the target domain with the help of the source domain, which is widely used and explored in real-w…
Group based Personalized Search by Integrating Search Behaviour and Friend Network
Yujia Zhou, Zhicheng Dou, Bingzheng Wei +1
The key to personalized search is to build the user profile based on historical behaviour. To deal with the users who lack historical data, group based personalized models were pro…
MIC: Model-agnostic Integrated Cross-channel Recommenders
Yujie Lu, Ping Nie, Shengyu Zhang +4
Semantically connecting users and items is a fundamental problem for the matching stage of an industrial recommender system. Recent advances in this topic are based on multi-channe…
Personalized Transfer of User Preferences for Cross-domain Recommendation
Yongchun Zhu, Zhenwei Tang, Yudan Liu +5
Cold-start problem is still a very challenging problem in recommender systems. Fortunately, the interactions of the cold-start users in the auxiliary source domain can help cold-st…
USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence
Jing Yao, Zhicheng Dou, Ruobing Xie +3
Search and recommendation are the two most common approaches used by people to obtain information. They share the same goal -- satisfying the user's information need at the right t…
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
Yongchun Zhu, Yudan Liu, Ruobing Xie +6
In recommender systems and advertising platforms, marketers always want to deliver products, contents, or advertisements to potential audiences over media channels such as display,…