## 1 Introduction  
After the COVID-19 outbreak and dire need for social distancing, there has been a tremendous upsurge in the demands for online services and technologies. In South Korea, \"untact\" has become a key plank after the country's economy was severely affected by the pandemic. Untact is a Konglish term which means \"no contact\" and it envisions a way of interaction that involves excessive online connections among people and replaces humans with machines [1; 2]. Due to COVID-19, there is a sudden shift of users from offline to online mediums, resulting in high demands for online services such as online shopping, online food delivery, online classes, virtual meetings, and online medical consultation, etc., [3]. In South Korea, with the expanding untact culture, there was recorded 15.02% hike in the usage of food delivery apps from 2.73 million users to 3.14 million users just in the span of 2-3 weeks. Similarly, in the same period of time, the number of online shopping users increased by 7% [4]. All the biggest shopping marts such as Homeplus, Lottemart, and Emart have witnessed a sudden rise in the online members in a very short period, requiring them to ramp up their delivery services. This expansion of online customers is not just limited to shopping and food delivery, Netflix has also witnessed 12.8% increase in users just in the first half of February 2020 [4]. Recommendation systems enable multiple online businesses and web applications to select and acquire interesting information from the systems. Some popular applications are e-commerce, retail, media, banking, telecom, utilities, etc. Therefore, the current pandemic situation has also accelerated the need for recommendation systems which enable users to make decisions in a relatively short time. There are other reasons as well for the demand for recommendation systems such as the rapidly growing internet applications generate a huge amount of data that can easily overload the users with an abundance of information. Recommendation systems become fundamental for redeeming users from such tiresomeness by alleviating the challenges caused by information overload. These recommendation systems effortlessly become a reason for the increase in sales. For example, Amazon and Netflix are taking advantage of recommendation systems by witnessing a 35% increase of purchases and a 75% increase of views on Amazon and Netflix, respectively [5]. Recommendation systems are a specific type of information filtering, which makes use of user's past activities and user comparisons to produce a random of information list that is customized according to the end user's requirements and preferences. The majority of existing recommendation frameworks depends on the user's historical data. Nonetheless, in numerous situations, the identity of the user might be unknown and only the actions of the user in the ongoing session are known. There are some session-based recommendation techniques proposed to handle these scenarios where the system can predict the next action of the user, e.g., in case of product recommendation the next click of the user on an item can be predicted based on his previous sequences of clicks. In this paper, we propose a product recommendation system for an e-shopping small known as ejeju small. This recommendation system uses the history data of user i.e., click sequences and recommend the item that the user is most likely to purchase. The state-of-the-art methods for recommendation systems assumes that user preferences and history data is completely known. However, in the real world, user preferences are very dynamic and can alter based on the user's mood. Therefore, the smart and intelligent modeling of user behaviors is required. We propose a Graph Convolutional Neural Network-based recommendation model that overcomes the limitations of previously proposed methods and it also considers the network structure. The proposed method optimizes the network structure for better representation of nodes.