GraphShop: Graph-based Approach for Shop-type Recommendation

Abstract

It is essential to predict the popularity of a particular shop type when investors decide which type of shops to open at a given location. Existing shop-type recommender systems have approached this problem by building a region type matrix and analyzing the relationship between different regions and shop types. However, these methods make recommendations for each region, thus having diffculty analyzing a specfic shop, especially near the two regions' borders. To tackle this challenge, we propose a novel Graph Neural Network (GNN) model, called GraphShop, to represent shops as nodes in a graph and analyze each shop without assigning it to a region. As it is diffcult to find the influential neighbors, we propose two aggregation methods, DistanceModule and TypeModule, in GraphShop. DistanceModule aggregates unordered nearby shops in every zone and fillters them from the remote zones. TypeModule reorders the nearby shops based on their types and considers the interaction of different types. Furthermore, to address the lack of open shop-type recommendation datasets, we build a qualitative and large-scale dataset collected from a review website and location-based services. It contains most, if not all, shops in a region and is large and diverse, by containing 53,182 shops with 122 types. Our dataset is available at https://github.com/BoSamothrace/GraphShop. Through the experimental results, we demonstrate that our method outperforms the existing state-of-the-art methods for shop type recommendation by a factor of up to 37%.