53 citations · 150 across the 15 of their papers we have counts for
9 papers · 1 filter
MMGRec: Multimodal Generative Recommendation with Transformer Model
Han Liu, Yinwei Wei, Xuemeng Song +3
Multimodal recommendation aims to recommend user-preferred candidates based on her/his historically interacted items and associated multimodal information. Previous studies commonl…
Attribute-driven Disentangled Representation Learning for Multimodal Recommendation
Zhenyang Li, Fan Liu, Yinwei Wei +3
Recommendation algorithms forecast user preferences by correlating user and item representations derived from historical interaction patterns. In pursuit of enhanced performance, m…
MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation
Yunshan Ma, Yingzhi He, Xiang Wang +4
Bundle recommendation seeks to recommend a bundle of related items to users to improve both user experience and the profits of platform. Existing bundle recommendation models have…
Leveraging Multimodal Features and Item-level User Feedback for Bundle Construction
Yunshan Ma, Xiaohao Liu, Yinwei Wei +3
Automatic bundle construction is a crucial prerequisite step in various bundle-aware online services. Previous approaches are mostly designed to model the bundling strategy of exis…
Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation
Wei Ji, Xiangyan Liu, An Zhang +3
Multi-modal recommendation systems, which integrate diverse types of information, have gained widespread attention in recent years. However, compared to traditional collaborative f…
Privacy-Preserving Synthetic Data Generation for Recommendation Systems
Fan Liu, Zhiyong Cheng, Huilin Chen +3
Recommendation systems make predictions chiefly based on users' historical interaction data (e.g., items previously clicked or purchased). There is a risk of privacy leakage when c…