1 citations · 1 across the 2 of their papers we have counts for
6 papers
FilterLLM: Text-To-Distribution LLM for Billion-Scale Cold-Start Recommendation
Ruochen Liu, Hao Chen, Yuanchen Bei +6
Large Language Model (LLM)-based cold-start recommendation systems continue to face significant computational challenges in billion-scale scenarios, as they follow a "Text-to-Judgm…
Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap
Weizhi Zhang, Yuanchen Bei, Liangwei Yang +15
Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recomm…
Feedback Reciprocal Graph Collaborative Filtering
Weijun Chen, Yuanchen Bei, Qijie Shen +3
Collaborative filtering on user-item interaction graphs has achieved success in the industrial recommendation. However, recommending users' truly fascinated items poses a seesaw di…
Macro Graph Neural Networks for Online Billion-Scale Recommender Systems
Hao Chen, Yuanchen Bei, Qijie Shen +6
Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational comp…
Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering
Yijie Zhang, Yuanchen Bei, Shiqi Yang +4
Graph collaborative filtering, which learns user and item representations through message propagation over the user-item interaction graph, has been shown to effectively enhance re…
Modeling Spatiotemporal Periodicity and Collaborative Signal for Local-Life Service Recommendation
Huixuan Chi, Hao Xu, Mengya Liu +4
Online local-life service platforms provide services like nearby daily essentials and food delivery for hundreds of millions of users. Different from other types of recommender sys…