Utilizing multi-source data in popularity prediction for shop-type recommendation

It is important for an investor to determine the most suitable shop type (e.g., restaurant, cafe) given a location. Traditionally, investors determine shop types based on their subjective judgments and perceptions. However, insufficient information and cognitive limitation often lead to flawed decisions and increase investment risks. With advances in information technology, multi-source heterogeneous information and big data analytics can be utilized to provide support for making such a decision. In this paper, we propose a novel shop-type recommendation method that suggests a suitable shop type based on multi-source information collected from a business review site, a location-based navigation system and a mobile carrier. Specifically, our method first constructs the location-type matrix to alleviate the problem of incomplete data and models the location profile by considering internal and external features simultaneously. In particular, a hybrid similarity model is proposed to integrate the location profile and the commercial structure into a unified framework. Then, a location-based collaborative filtering method is developed to predict shop popularity and suggest a suitable shop type. Finally, we demonstrate the effectiveness of our method compared to several benchmark methods by applying it to a real-world dataset from China.