Exploiting diffusion-based structured learning for item interactions representations in multimodal recommender systems

doi:10.1016/j.ipm.2025.104075

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Exploiting diffusion-based structured learning for item interactions representations in multimodal recommender systems

2025-05-01

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Khan, Nikhat *

Sisodia, Dilip Singh

DOI：10.1016/j.ipm.2025.104075

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Abstract

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Multimodal Recommender Systems (MRS) enhance the performance of recommendations by utilizing different item information, such as text, images, and audio. Existing non-graph-based MRS techniques combine embeddings (i.e., id and multimodal embedding) but ignore indirect and higher-order interactions. Graph-based MRS approaches use graph sparsification (GS) to construct item graphs and graph convolutional networks (GCNs) for higher-order interactions. However, GS reduces the item graph size, while GCNs ignore specific information due to their predefined weights. Hence, to mitigate the mentioned issues, this study proposes a Diffusionbased Structured Learning technique for Multimodal Recommender Systems (DSL-MRS) that improves the latent item graph information flow while maintaining its structure. Additionally, we used a graph attention neural network (GANN) to represent complex higher-order item-item interactions and implemented an attention mechanism to prioritize relevant nodes by assigning weights to neighbour. Also, for optimization, a Weighted Approximate-Rank pairwise (WARP) loss function has been used to prioritize predictions for observed items over those for unspecified items. To demonstrate the advantage of DSL-MRS, we conducted extensive experiments on three publicly available categories of Amazon datasets. The experimental findings showed that the proposed approach led to an average improvement of 5.8 % in R@20, 8.7 % in precision@20,7.8 % in NDCG@20 and 8.8 % in F-score@20 compared to the baseline model. Ablation studies demonstrate the value and efficacy of DSL-MRS, as its components degrade performance when removed.

Keywords:

Multimodal recommender system

Attention mechanism

Graph structure learning

Graph diffusion

Interaction graph