Semantic Knowledge Discovery for User Profiling for Location-Based Recommender Systems

Abstract

Abstract This paper introduces a purposed Location-based Recommender System (LBRS) that combines sentiment analysis and topic modelling techniques to improve user profiling for enhancing recommendations of Points of Interest (POIs). Using additional feature extraction, we built user profiles froma Foursquare dataset to evaluate our model and provide recommendations based on user opinions toward venues. Our combined model performed favourably against the baseline models, with an overall improved accuracy of 0.67. The limitations were the use of one dataset and that user profiles were constructed using predicted emotions extracted as features from review data with topic modelling, rather than literal user emotions. Nevertheless, this provides a step forward in user profile and emotion scoring, contributing further to the development of LBRS in the Tourism domain.

Keywords

• Location-based recommender system
• Foursquare
• sentiment analysis
• topic modelling
• venue recommendation
• places of interest