Knowledge-aware Diffusion-Enhanced Multimedia Recommendation

TL;DR

This paper introduces KDiffE, a novel multimedia recommendation framework that combines attention-aware graph neural networks and guided diffusion to incorporate knowledge graphs, improving recommendation accuracy.

Contribution

The paper proposes a new architecture integrating attention-aware GNNs and a guided diffusion model for constructing knowledge graphs in multimedia recommendation systems.

Findings

KDiffE outperforms state-of-the-art methods on three multimedia datasets.

The attention-aware matrix effectively captures user-item importance.

Guided diffusion enhances semantic information with less noise.

Abstract

Multimedia recommendations aim to use rich multimedia content to enhance historical user-item interaction information, which can not only indicate the content relatedness among items but also reveal finer-grained preferences of users. In this paper, we propose a Knowledge-aware Diffusion-Enhanced architecture using contrastive learning paradigms (KDiffE) for multimedia recommendations. Specifically, we first utilize original user-item graphs to build an attention-aware matrix into graph neural networks, which can learn the importance between users and items for main view construction. The attention-aware matrix is constructed by adopting a random walk with a restart strategy, which can preserve the importance between users and items to generate aggregation of attention-aware node features. Then, we propose a guided diffusion model to generate strongly task-relevant knowledge graphs with less noise for constructing a knowledge-aware contrastive view, which utilizes user embeddings with an edge connected to an item to guide the generation of strongly task-relevant knowledge graphs for enhancing the item's semantic information. We perform comprehensive experiments on three multimedia datasets that reveal the effectiveness of our KDiffE and its components on various state-of-the-art methods. Our source codes are available https://github.com/1453216158/KDiffE.