Revisiting SVD to generate powerful Node Embeddings for Recommendation Systems
This work addresses recommendation system accuracy for users and items, but it is incremental as it builds on existing SVD methods with a simple extension.
The paper tackles the problem of generating node embeddings for recommendation systems by revisiting Singular Value Decomposition (SVD) and extending it to include two-hop neighbors, resulting in improved performance that beats many state-of-the-art methods by up to 10% on two out of three datasets.
Graph Representation Learning (GRL) is an upcoming and promising area in recommendation systems. In this paper, we revisit the Singular Value Decomposition (SVD) of adjacency matrix for embedding generation of users and items and use a two-layer neural network on top of these embeddings to learn relevance between user-item pairs. Inspired by the success of higher-order learning in GRL, we further propose an extension of this method to include two-hop neighbors for SVD through the second order of the adjacency matrix and demonstrate improved performance compared with the simple SVD method which only uses one-hop neighbors. Empirical validation on three publicly available datasets of recommendation system demonstrates that the proposed methods, despite being simple, beat many state-of-the-art methods and for two of three datasets beats all of them up to a margin of 10%. Through our research, we want to shed light on the effectiveness of matrix factorization approaches, specifically SVD, in the deep learning era and show that these methods still contribute as important baselines in recommendation systems.