Estimating Revenues of Seoul Commercial Alley Services using Tensor Decomposition & Generating Recommendation System

As for people who wish to start their own businesses, their concerns are whether they could survive during their operations because most stores or services in Seoul are not able to survive for more than a few year possibly due to the poor decision of location/service to start. In order to solve this problem, using big data could be helpful to increase the survival rate. Singular Value Decomposition (SVD) has been widely used in finding the similarity between all pairs of alleys and obtaining predictions from unknown relevance scores. Since tensor decomposition is the extension of SVD for multi-dimensional data, using this method to find the similarity between all pair of alleys could be the solution of increasing survival rate. This paper aims to generate good prediction tensor, TENSORCABS, that is able to recommend users appropriate alley location to start their businesses or the appropriate services to start at the user's desired location. Both CP and Tucker decompositions are used and compared to evaluate which method has better performance. Also, r-square for regression problem and precision & recall for top-k recommendation performance are used to evaluate the TENSORCABS. As results, actual and predicted values are good-fitted, and prediction tensor performs well on the top-k recommendation. In addition, Tucker outperforms CP for this situation. Therefore, the proposed method has advantages that can handle high-dimensional data and can use decomposition for recommendations of various perspectives. With this method, users are able to obtain recommendations of the appropriate alleys with predicted revenues for opening any business service or the appropriate services with predicted revenues on a user's desired alley location.