4 citations · 10 across the 13 of their papers we have counts for
13 papers
When and How to Ask: Dynamic Preference Elicitation Strategies for Conversational Recommendation
Feng Xia, Shuo Zhang, Xi Wang
Conversational Recommender Systems (CRSs) are interactive systems that use multi-turn natural language dialogue to understand evolving user preferences and provide personalized rec…
Interplay: Training Independent Simulators for Reference-Free Conversational Recommendation
Jerome Ramos, Feng Xia, Xi Wang +4
Training conversational recommender systems (CRS) requires extensive dialogue data, which is challenging to collect at scale. To address this, researchers have used simulated user-…
Explaining Synergistic Effects in Social Recommendations
Yicong Li, Shan Jin, Qi Liu +6
In social recommenders, the inherent nonlinearity and opacity of synergistic effects across multiple social networks hinders users from understanding how diverse information is lev…
ORCA: Mitigating Over-Reliance for Multi-Task Dwell Time Prediction with Causal Decoupling
Huishi Luo, Fuzhen Zhuang, Yongchun Zhu +6
Dwell time (DT) is a critical post-click metric for evaluating user preference in recommender systems, complementing the traditional click-through rate (CTR). Although multi-task l…
Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback
Guipeng Xv, Xinyu Li, Ruobing Xie +5
Multi-modal recommender systems (MRSs) are pivotal in diverse online web platforms and have garnered considerable attention in recent years. However, previous studies overlook the…
Multi-Granularity Click Confidence Learning via Self-Distillation in Recommendation
Chong Liu, Xiaoyang Liu, Lixin Zhang +2
Recommendation systems rely on historical clicks to learn user interests and provide appropriate items. However, current studies tend to treat clicks equally, which may ignore the…
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…
A Survey on Fairness-aware Recommender Systems
Di Jin, Luzhi Wang, He Zhang +4
As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, whic…
Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender
Shuai Yang, Lixin Zhang, Feng Xia +1
There are roughly three stages in real industrial recommendation systems, candidates generation (retrieval), ranking and reranking. Individual-level diversity and system-level dive…
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…
Pruning Pre-trained Language Models Without Fine-Tuning
Ting Jiang, Deqing Wang, Fuzhen Zhuang +2
To overcome the overparameterized problem in Pre-trained Language Models (PLMs), pruning is widely used as a simple and straightforward compression method by directly removing unim…
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…
Long Short-Term Temporal Meta-learning in Online Recommendation
Ruobing Xie, Yalong Wang, Rui Wang +4
An effective online recommendation system should jointly capture users' long-term and short-term preferences in both users' internal behaviors (from the target recommendation task)…