Towards building and evaluating a personalized location-based recommender system

Personalized location-based service recommendation is an important trend in the development of online ecommerce applications. In this work, we integrate the application of location-based service with recommendation technologies to present a hybrid recommendation model and a prototype system (HiPerData) to evaluate and measure the validity based on the Yelp dataset. In order to solve the four recommendation problems, we improve a predictive feature-based regression model, and combine the results of a set of collaborative filtering algorithms, which includes: SVD (Singular value decomposition), SVR (Support vector regression), SGD (Stochastic gradient descent), etc. Unlike previous approaches, we apply multiple methods to pre- and post-process the dataset and predict ratings, for example, a weighted pairwise preference regression for cold start problems, etc. We enhance the neighborhood-based approach leading to a substantial improvement of prediction accuracy. Our method gave the best overall results with a root mean square error of 1.22724.