3 papers
cs.IR2026
From Token Generation to Item Ranking: Direct Generative Recommendation with Semantic IDs
Yuanbo Zhao, Ruochen Liu, Senzhang Wang +6
Generative recommendation formulates item recommendation as a token-level generation task, where Semantic IDs (SIDs) represents each item as a sequence of discrete tokens. However,…
cs.IR2025
Macro Graph of Experts for Billion-Scale Multi-Task Recommendation
Hongyu Yao, Zijin Hong, Hao Chen +6
Graph-based multi-task learning at billion-scale presents a significant challenge, as different tasks correspond to distinct billion-scale graphs. Traditional multi-task learning m…
cs.IR2024
Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks
Yijie Zhang, Yuanchen Bei, Hao Chen +6
Representing information of multiple behaviors in the single graph collaborative filtering (CF) vector has been a long-standing challenge. This is because different behaviors natur…