Abstract

With the ever-growing amount of online data and information, recommender systems are becoming overwhelmingly popular as an adequate approach for overcoming the challenge of information overload. Artificial Intelligence (AI) and Deep Learning (DL) have accumulated significant interest in many research areas, and recommender systems are one of them. In this paper, a Graph Convolutional Neural Network (GCNN)-based approach was used for online product recommendation. Graph-based methods have undergone substantial consideration for several recommendation tasks, with effective results. However, handling the computational complexities and training large datasets remain a challenge for such a model. Even though they are useful, the excessive measure of the model’s boundaries obstructs their applications in real-world recommender frameworks to a great extent. The recursive way of generating neighbor node embeddings for each node in the graph makes it more challenging to train a deep and large GCNN model. Therefore, we propose a model that incorporates measures of similarity between two different nodes, and these similarity measures help us to sample the neighbors beforehand. We estimate the similarity based on their interaction probability distribution with other nodes. We use KL divergence on different probability distributions to find the distance between them. This way, we set a threshold criterion for neighbor selection and generate other clusters. These clusters are then converted to subgraphs and are used as input for the proposed GCNN model. This approach simplifies the task of neighbor sampling for GCNN, and hence, we can observe a significant improvement in the computational complexity of the GCNN model. Finally, we compared the results with those for the previously proposed OpGCN model, basic GCNN model, and other traditional approaches such as collaborative filtering and probabilistic matrix factorization. The experiments showed that the complexity and computational time were decreased by estimating the similarity among nodes and sampling the nodes before training.

Highlights

  • Recommender systems have been at the cutting edge and are becoming overwhelmingly popular in different fields

  • We present the root mean squared error rate, accuracy with respect to the hidden units (HU), and accuracy of the training and testing phase of our proposed Graph Convolutional Neural Network (GCNN)

  • We introduced a graph-CNN-based model for online product recommendation

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Summary

Introduction

Recommender systems have been at the cutting edge and are becoming overwhelmingly popular in different fields. Users are presented with enormous information and choices to make every day, which complicates the decision-making process These intelligent systems aim to rescue such users from the daily fatigue of selection. ECommerce has been becoming increasingly saturated by online customers, and companies are researching possible ways to develop trust and capture customers’ loyalty by personalizing the shopping experience. The COVID-19 pandemic has accelerated and shifted consumers more towards the digital world. Multiple surveys suggest that the shift towards the digital world and online shopping will outlast the COVID-19 pandemic and requires the development of significant recommendation engines to stay in the competition for and retain customers [2]

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