User Embedding based Neighborhood Aggregation Method for Inductive Recommendation

• URL: http://arxiv.org/abs/2102.07575v2
• Date: Tue, 16 Feb 2021 12:43:13 GMT
• Title: User Embedding based Neighborhood Aggregation Method for Inductive Recommendation
• Authors: Rahul Ragesh, Sundararajan Sellamanickam, Vijay Lingam, Arun Iyer and Ramakrishna Bairi
• Abstract summary: We consider the problem of learning latent features (aka embedding) for users and items in a recommendation setting. Recent methods using graph convolutional networks (e.g., LightGCN) achieve state-of-the-art performance. We propose a graph convolutional network modeling approach for collaborative filtering CF-GCN.
• Score: 0.48598200320383667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the problem of learning latent features (aka embedding) for users and items in a recommendation setting. Given only a user-item interaction graph, the goal is to recommend items for each user. Traditional approaches employ matrix factorization-based collaborative filtering methods. Recent methods using graph convolutional networks (e.g., LightGCN) achieve state-of-the-art performance. They learn both user and item embedding. One major drawback of most existing methods is that they are not inductive; they do not generalize for users and items unseen during training. Besides, existing network models are quite complex, difficult to train and scale. Motivated by LightGCN, we propose a graph convolutional network modeling approach for collaborative filtering CF-GCN. We solely learn user embedding and derive item embedding using light variant CF-LGCN-U performing neighborhood aggregation, making it scalable due to reduced model complexity. CF-LGCN-U models naturally possess the inductive capability for new items, and we propose a simple solution to generalize for new users. We show how the proposed models are related to LightGCN. As a by-product, we suggest a simple solution to make LightGCN inductive. We perform comprehensive experiments on several benchmark datasets and demonstrate the capabilities of the proposed approach. Experimental results show that similar or better generalization performance is achievable than the state of the art methods in both transductive and inductive settings.

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