EDGE-Rec: Efficient and Data-Guided Edge Diffusion For Recommender Systems Graphs

• URL: http://arxiv.org/abs/2409.14689v1
• Date: Mon, 23 Sep 2024 03:23:20 GMT
• Title: EDGE-Rec: Efficient and Data-Guided Edge Diffusion For Recommender Systems Graphs
• Authors: Utkarsh Priyam, Hemit Shah, Edoardo Botta,
• Abstract summary: We propose a new attention mechanism to take advantage of real-valued interaction weights as well as user and item features directly. We train a novel Graph Diffusion Transformer GDiT architecture to iteratively denoise the weighted interaction matrix of the user-item interaction graph directly. Inspired by the recent progress in text-conditioned image generation, our method directly produces user-item rating predictions on the same scale as the original ratings.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most recommender systems research focuses on binary historical user-item interaction encodings to predict future interactions. User features, item features, and interaction strengths remain largely under-utilized in this space or only indirectly utilized, despite proving largely effective in large-scale production recommendation systems. We propose a new attention mechanism, loosely based on the principles of collaborative filtering, called Row-Column Separable Attention RCSA to take advantage of real-valued interaction weights as well as user and item features directly. Building on this mechanism, we additionally propose a novel Graph Diffusion Transformer GDiT architecture which is trained to iteratively denoise the weighted interaction matrix of the user-item interaction graph directly. The weighted interaction matrix is built from the bipartite structure of the user-item interaction graph and corresponding edge weights derived from user-item rating interactions. Inspired by the recent progress in text-conditioned image generation, our method directly produces user-item rating predictions on the same scale as the original ratings by conditioning the denoising process on user and item features with a principled approach.

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