9 papers · 1 filter
TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation
Yangchen Zeng, Hao Peng, Rongfeng Guo +3
We introduce TriAlignGR, a unified multitask-multimodal framework for generative recommendation that establishes two-stage multimodal semantic propagation: (i) encoding visual sema…
Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal Generative Recommendation
Yangchen Zeng, Jinze Wang
Semantic IDs (SIDs) provide the discrete item vocabulary used by generative recommendation, but their quality depends on what item evidence is preserved before quantization. In pro…
Meta-Modal Agent: Sequential Evidence Routing for Missing-Modality Candidate Reranking
Jinze Wang, Yangchen Zeng, Tiehua Zhang +5
Missing modalities cause severe failures in multimodal recommender systems. User histories, item text, and visual evidence are frequently absent during cold-start scenarios, exactl…
Agent4POI: Agentic Context-Conditioned Affordance Reasoning for Multimodal Point-of-Interest Recommendation
Jinze Wang, Yangchen Zeng, Tiehua Zhang +5
We introduce Agent4POI, the first POI recommendation framework that generates context-conditioned multimodal representations at recommendation time, rather than relying on static P…
ADS-POI: Agentic Spatiotemporal State Decomposition for Next Point-of-Interest Recommendation
Zhenyu Yu, Chunlei Meng, Yangchen Zeng +2
Next point-of-interest (POI) recommendation requires modeling user mobility as a spatiotemporal sequence, where different behavioral factors may evolve at different temporal and sp…
CaST-POI: Candidate-Conditioned Spatiotemporal Modeling for Next POI Recommendation
Zhenyu Yu, Chunlei Meng, Yangchen Zeng +3
Next Point-of-Interest (POI) recommendation ranks a user's likely next location based on check-in history. Most recent rankers compress the trajectory into a single user vector and…