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Efficient Graph based Recommender System with Weighted Averaging of Messages

arXiv 2209.15238 ↗First posted 30 Sep 2022cs.LGcited by 0

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Abstract (arXiv)

We showcase a novel solution to a recommendation system problem where we face a perpetual soft item cold start issue. Our system aims to recommend demanded products to prospective sellers for listing in Amazon stores. These products always have only few interactions thereby giving rise to a perpetual soft item cold start situation. Modern collaborative filtering methods solve cold start using content attributes and exploit the existing implicit signals from warm start items. This approach fails in our use-case since our entire item set faces cold start issue always. Our Product Graph has over 500 Million nodes and over 5 Billion edges which makes training and inference using modern graph algorithms very compute intensive. To overcome these challenges we propose a system which reduces the dataset size and employs an improved modelling technique to reduce storage and compute without loss in performance. Particularly, we reduce our graph size using a filtering technique and then exploit this reduced product graph using Weighted Averaging of Messages over Layers (WAML) algorithm. WAML simplifies training on large graphs and improves over previous methods by reducing compute time to 1/7 of LightGCN and 1/26 of Graph Attention Network (GAT) and increasing recall@100 by 66% over LightGCN and 2.3x over GAT.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 27 references that are in the map. First posted on arXiv 30 Sep 2022.

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References · 19

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  12. 2019Graph Neural Networks for Social Recommendationcited by 714
7 more
  1. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 676
  2. 2019Revisiting Graph Neural Networks: All We Have is Low-Pass Filterscited by 261
  3. 2021A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directionscited by 144
  4. 2018Billion-scale Commodity Embedding for E-commerce Recommendation in Alibabacited by 105
  5. 2021Contrastive Learning for Cold-Start Recommendationcited by 79
  6. 2021Graph Learning based Recommender Systems: A Reviewcited by 59
  7. 2022An Introduction to Matrix factorization and Factorization Machines in Recommendation System, and Beyondcited by 5

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