7 papers
HyperAgent4POI: Dynamic Semantic Message Passing on Multi-Agent Hypergraphs for Missing-Modality Recommendation
Jinze Wang, Yuze Liu, Tiehua Zhang +2
Next Point-of-Interest (POI) recommendation benefits from textual and visual content that describes venue semantics, yet such content is often incomplete in real-world services. Mi…
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…
Route What Remains: A Meta-Modal Agent for Missing-Modality Candidate Reranking in Recommender Systems
Jinze Wang, Yangchen Zeng, Tiehua Zhang +5
Missing-modality recommenders usually reconstruct absent representations, although the observed evidence may not determine the missing content. We formulate candidate reranking as…
DeepInterestGR: Mining Deep Multi-Interest Using Multi-Modal LLMs for Generative Recommendation
Yangchen Zeng, Guangyuan Dong, Zhenyu Yu +4
We introduce DeepInterestGR, a novel framework that integrates deep interest mining into the generative recommendation pipeline. This addresses the "Shallow Interest" problem: exis…
HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI Recommendation
Jinze Wang, Tiehua Zhang, Lu Zhang +3
Next Point-of-Interest (POI) recommendation aims to predict users' next locations by leveraging historical check-in sequences. Although existing methods have shown promising result…
Meta-learning enhanced next POI recommendation by leveraging check-ins from auxiliary cities
Jinze Wang, Lu Zhang, Zhu Sun +1
Most existing point-of-interest (POI) recommenders aim to capture user preference by employing city-level user historical check-ins, thus facilitating users' exploration of the cit…