Mining Latent Structures for Multimedia Recommendation

Abstract

Multimedia content is of predominance in the modern Web era. Investigating how users interact with multimodal items is a continuing concern within the rapid development of recommender systems. The majority of previous work focuses on modeling user-item interactions with multimodal features included as side information. However, this scheme is not well-designed for multimedia recommendation. Specifically, only collaborative item-item relationships are implicitly modeled through high-order item-user-item relations. Considering that items are associated with rich contents in multiple modalities, we argue that the latent semantic item-item structures underlying these multimodal contents could be beneficial for learning better item representations and further boosting recommendation. To this end, we propose a LATent sTructure mining method for multImodal reCommEndation, which we term LATTICE for brevity. To be specific, in the proposed LATTICE model, we devise a novel modality-aware structure learning layer, which learns item-item structures for each modality and aggregates multiple modalities to obtain latent item graphs. Based on the learned latent graphs, we perform graph convolutions to explicitly inject high-order item affinities into item representations. These enriched item representations can then be plugged into existing collaborative filtering methods to make more accurate recommendations. Extensive experiments on three real-world datasets demonstrate the superiority of our method over state-of-the-art multimedia recommendation methods and validate the efficacy of mining latent item-item relationships from multimodal features.

arXiv commentAccepted to ACM Multimedia 2021. Authors' version

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• Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal Recommendation
• Formalizing Multimedia Recommendation through Multimodal Deep Learning
• A Multi-Modal Latent-Features based Service Recommendation System for the Social Internet of Things
• Relation-aware Heterogeneous Graph for User Profiling
• Predicting Information Pathways Across Online Communities
• Pareto Invariant Representation Learning for Multimedia Recommendation
• Diffusion-based Multi-modal Synergy Interest Network for Click-through Rate Prediction
• Code Recommendation for Open Source Software Developers
• Causality-Inspired Fair Representation Learning for Multimodal Recommendation
• MSCRS: Multi-modal Semantic Graph Prompt Learning Framework for Conversational Recommender Systems
• Collaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendation
• A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios
• Modality Alignment with Multi-scale Bilateral Attention for Multimodal Recommendation
• I${}^3$-MRec: Invariant Learning with Information Bottleneck for Incomplete Modality Recommendation
• Preliminary Evaluation of the Test-Time Training Layers in Recommendation System (Student Abstract)