DSGRec: dual-path selection graph for multimodal recommendation
Zihao Liu, Wen Qu
TL;DR
This paper introduces DSGRec, a new recommendation system that improves accuracy by combining user behavior and multi-modal data through a dual-path graph architecture.
Contribution
The novel dual-path selection architecture enhances collaboration between user behavior and multi-modal information in recommendation systems.
Findings
DSGRec outperforms state-of-the-art methods on three benchmark datasets.
The dual-path design improves modeling of user behavior and multi-modal signals.
Contrastive learning tasks help align auxiliary signals with user-item interactions.
Abstract
With the advancement of digital streaming technology, multi-modal recommendation systems have gained significant attention. Current graph-based multi-modal recommendation approaches typically model user interests using either user interaction signals or multi-modal item information derived from heterogeneous graphs. Although methods based on graph convolutional networks (GCNs) have achieved notable success, they still face two key limitations: (1) the narrow interpretation of interaction information, leading to incomplete modeling of user behavior, and (2) a lack of fine-grained collaboration between user behavior and multi-modal information. To address these issues, we propose a novel method by decomposing interaction information into two distinct signal pathways, referred to as a dual-path selection architecture, named Dual-path Selective Graph Recommender (DSGRec). DSGRec is designed…
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Taxonomy
TopicsRecommender Systems and Techniques · Advanced Graph Neural Networks · Multimodal Machine Learning Applications