9 citations · 14 across the 16 of their papers we have counts for
16 papers
Bridging Passive and Active: Enhancing Conversation Starter Recommendation via Active Expression Modeling
Yiqing Wu, Haoming Li, Guanyu Jiang +4
Large Language Model (LLM)-driven conversational search is shifting information retrieval from reactive keyword matching to proactive, open-ended dialogues. In this context, Conver…
TextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer
Yiwen Chen, Yiqing Wu, Huishi Luo +3
Graph-based recommendation has achieved great success in recent years. The classical graph recommendation model utilizes ID embedding to store essential collaborative information.…
CAT-ID: Category-Tree Integrated Document Identifier Learning for Generative Retrieval In E-commerce
Xiaoyu Liu, Fuwei Zhang, Yiqing Wu +6
Generative retrieval (GR) has gained significant attention as an effective paradigm that integrates the capabilities of large language models (LLMs). It generally consists of two s…
A Soft-partitioned Semi-supervised Collaborative Transfer Learning Approach for Multi-Domain Recommendation
Xiaoyu Liu, Yiqing Wu, Ruidong Han +3
In industrial practice, Multi-domain Recommendation (MDR) plays a crucial role. Shared-specific architectures are widely used in industrial solutions to capture shared and unique a…
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…
CDC: Causal Domain Clustering for Multi-Domain Recommendation
Huishi Luo, Yiqing Wu, Yiwen Chen +2
Multi-domain recommendation leverages domain-general knowledge to improve recommendations across several domains. However, as platforms expand to dozens or hundreds of scenarios, t…
Measure Domain's Gap: A Similar Domain Selection Principle for Multi-Domain Recommendation
Yi Wen, Yue Liu, Derong Xu +9
Multi-Domain Recommendation (MDR) achieves the desirable recommendation performance by effectively utilizing the transfer information across different domains. Despite the great su…
One for Dozens: Adaptive REcommendation for All Domains with Counterfactual Augmentation
Huishi Luo, Yiwen Chen, Yiqing Wu +2
Multi-domain recommendation (MDR) aims to enhance recommendation performance across various domains. However, real-world recommender systems in online platforms often need to handl…
Unified Dual-Intent Translation for Joint Modeling of Search and Recommendation
Yuting Zhang, Yiqing Wu, Ruidong Han +7
Recommendation systems, which assist users in discovering their preferred items among numerous options, have served billions of users across various online platforms. Intuitively,…
DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback
Yiqing Wu, Ruobing Xie, Zhao Zhang +5
The graph-based recommendation has achieved great success in recent years. However, most existing graph-based recommendations focus on capturing user preference based on positive e…
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…
Modeling Dual Period-Varying Preferences for Takeaway Recommendation
Yuting Zhang, Yiqing Wu, Ran Le +7
Takeaway recommender systems, which aim to accurately provide stores that offer foods meeting users' interests, have served billions of users in our daily life. Different from trad…
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…
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…
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-…