1 citations · 1 across the 5 of their papers we have counts for
6 papers
PI2I: A Personalized Item-Based Collaborative Filtering Retrieval Framework
Shaoqing Wang, Yingcai Ma, Kairui Fu +4
Efficiently selecting relevant content from vast candidate pools is a critical challenge in modern recommender systems. Traditional methods, such as item-to-item collaborative filt…
RecGPT-V2 Technical Report
Chao Yi, Dian Chen, Gaoyang Guo +32
Large language models (LLMs) have demonstrated remarkable potential in transforming recommender systems from implicit behavioral pattern matching to explicit intent reasoning. Whil…
Beyond Existing Retrievals: Cross-Scenario Incremental Sample Learning Framework
Tao Wang, Xun Luo, Jinlong Guo +4
The parallelized multi-retrieval architecture has been widely adopted in large-scale recommender systems for its computational efficiency and comprehensive coverage of user interes…
TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarios
Zida Liang, Changfa Wu, Dunxian Huang +9
Recommendation systems are essential tools in modern e-commerce, facilitating personalized user experiences by suggesting relevant products. Recent advancements in generative model…
RecGPT Technical Report
Chao Yi, Dian Chen, Gaoyang Guo +51
Recommender systems are among the most impactful applications of artificial intelligence, serving as critical infrastructure connecting users, merchants, and platforms. However, mo…
SlimGPT: Layer-wise Structured Pruning for Large Language Models
Gui Ling, Ziyang Wang, Yuliang Yan +1
Large language models (LLMs) have garnered significant attention for their remarkable capabilities across various domains, whose vast parameter scales present challenges for practi…