Graph Neural Network for Service Recommender System in Digital Service Marketplace

Abstract

The emergence of the platform economy has resulted in the decline of many traditional forms of doing business. Freelance work makes use of a platform to connect businesses or people with other businesses or persons in order to solve particular issues or deliver specific services in return for payment. The pairing process involves a buyer that needs work done, a platform that handles the algorithm, and a worker who is willing to do the job via the platform. This research argues that by efficiently pairing the talents of workers to the requirements of buyers, the platforms have the ability to expedite business operations for buyers, empower platform workers, and significantly improve the overall customer experience. Graph Convolutional Networks (GCNs) are inspired by CNNs and aim to expand the convolution operation from grid records to graph records, which in turn facilitates advances in the graph domain. In order to develop reliable and accurate embeddings for digital service recommendation, we employed a graph-based technique on a freelance platform dataset using the graph linkages of services and buyer data. We employed an aggregation-based inductive graph convolution network, namely, Graph SAmple and aggreGatE (GraphSAGE). It is a generalized inductive architecture that learns to construct embeddings for previously unknown data by sampling and combining attributes from a node's immediate neighborhood. We also applied PinSage, a stochastic Graph Convolutional Network (GCN) that can learn node embeddings in platform networks with many digital services. When a robust recommender system is used in digital service marketplace, it can offer promising results that may increase users' satisfaction with the service and boost the platform's ability to increase revenue.