## 1 Introduction  
Lately, recommender systems have been at the cutting edge and are becoming overwhelmingly popular in different fields. These systems are enticing a large community of researchers because of their noteworthy applications. Recommender systems primarily screen, organize, and proficiently convey significant data to ease the issue of data over-burden, which has become a possible issue for numerous internet clients. Users are presented with enormous information and choices to make every day, which complicates the decision-making process. Therefore, these intelligent systems aim to rescue such users from the daily fatigue of selection. Recently, 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. One of the core approaches to dealing with advancement is through the implementation of product recommendations. Researchshows that 56% of customers return to a website that provides personalized product recommendations that meet customers' expectations, and 75% of today's generation prefers a customized online shopping experience [1; 2]. The COVID-19 pandemic has accelerated and shifted consumers more towards the digital world. This online shift has made it easier to control the outbreak of COVID-19 and maintain social distancing and has enabled firms to extend their business and strengthen the economy. Necessities and daily use products have become more demanded than luxurious goods in the United States, where online grocery sales have increased by 16%, and in the EU, they have increased by 30% [3]. South Korea, remarkably, has seen an increase of 90% for the online sale of food products during the pandemic. According to South Korea's Ministry of Trade, Industry, and Energy (MOTIE), sales for offline markets took the worst hit, with a decrease of 21.4% in sales, whereas online markets with personalized recommendation engines surged by 92.5% [4]. 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]. Due to the massive availability of data and product information on the Internet, it becomes difficult to extract meaningful information to eliminate the tiresome decision-making process. Recommendation systems filter information relevant to the user based on a specific predefined set of rules. The recommendation engine uses data related to customer click patterns, the number of views for an item, reviews, ratings, the popularity of an item, the delivery time for the product, and many other factors to automatically present the list of the most related products without the customer needing to search for them. Recommendation systems are being used not only for products but also for endorsing services in the fields of banking, telecom, retail, and media. Collaborative filtering, one of the well-known product recommendation techniques, considers that customers having similar preferences for items will have identical click patterns and will end up buying many similar products. However, the sparsity of data requires more intelligent recommendation systems that consider intricate details to identify users' behavioral patterns and measure the similarity between users' activities. This paper proposes a session-based Graph Convolutional Neural Network (GCNN)-based product recommendation model that incorporates measures of similarity between multiple users to produce an optimized, accurate, and intelligent recommendation system. The input data for the recommendation system are acquired from an e-shopping mall known as eJeju mall. They consist of the users' historical data, and we utilize these data to form session-based graphs that exhibit the users' dynamic behavior. The session graphs are directed graphs generated based on each user's sequence of clicks in each session.