44 citations
- Kuaishou (China)5 papers
- City University of Hong KongHK2 papers
- Hong Kong Polytechnic UniversityHK2 papers
- Peng Cheng LaboratoryCN2 papers
- Tsinghua UniversityCN2 papers
- University of Chinese Academy of SciencesCN2 papers
- Beijing Institute of TechnologyCN1 paper
- Beijing Jiaotong UniversityCN1 paper
- Beijing Normal UniversityCN1 paper
- Beijing University of Posts and TelecommunicationsCN1 paper
- China Jiliang UniversityCN1 paper
- Chinese Academy of SciencesCN1 paper
7 papers · 1 filter
ConAlign: Conditional Alignment Framework for Balancing Biased and Unbiased Recommendation
Jingcheng Zhang, Yihan Wang, Qi Song +1
Industry recommender systems trained on observational data suffer from various biases that create filter bubbles, causing user interests to collapse into narrow categories and seve…
RECAP: Feedback-Driven Streaming Semantic User Profiles for Short-Video Recommendation
Ziyi Zhao, Xiaoyou Zhou, Xiao Lv +13
Language-based user profiles convert long behavioral histories into explicit semantic representations for recommendation. However, most profile generators are optimized in an open…
Coarse-to-Fine Long-term Interest Modeling for Generative Recommendation
Shiteng Cao, Junda She, Bin Zeng +9
Leveraging long-term user behavioral patterns is a key trajectory for enhancing the accuracy of modern recommender systems. While generative recommender systems have emerged as a t…
DiffGRM: Diffusion-based Generative Recommendation Model
Zhao Liu, Yichen Zhu, Yiqing Yang +7
Generative recommendation (GR) is an emerging paradigm that represents each item via a tokenizer as an n-digit semantic ID (SID) and predicts the next item by autoregressively gene…
MPFormer: Adaptive Framework for Industrial Multi-Task Personalized Sequential Retriever
Yijia Sun, Shanshan Huang, Linxiao Che +4
Modern industrial recommendation systems encounter a core challenge of multi-stage optimization misalignment: a significant semantic gap exists between the multi-objective optimiza…
Modeling User Fatigue for Sequential Recommendation
Nian Li, Xin Ban, Cheng Ling +6
Recommender systems filter out information that meets user interests. However, users may be tired of the recommendations that are too similar to the content they have been exposed…