Enhancing point-of-interest recommendation with deep learning: leveraging user reviews and geographic area features

Purpose With the increasing popularity of mobile devices in recent years, users can share their check-in behavior on social networks at any time or any place. However, with the increasing number of users sharing information on social networks and the Internet, users now find it challenging to find the information they need. Many studies have proposed Point-of-Interest (POI) recommender systems to address information overload, aiming to predict POIs based on POI tags, categories, geographic location, and users' check-in times. However, these features are too specific to represent the characteristics of users and POIs. Design/methodology/approach This work proposes a POI recommendation method based on user reviews and geographic area features (PRRG) to predict which POIs users may be interested in. The research framework includes review analysis and POI area analysis. The review analysis extracts topics, sentiment, and semantic features from user reviews to represent user preferences and POI features. The POI area analysis divides POIs into distinct areas and calculates area weights based on users' movement patterns. Finally, the weighted matrix factorization method is used to predict the POI ratings. It captures the semantics of user preferences expressed in text and users' sequential mobility traces. Findings The proposed method can extract various features to represent user preferences and POI features and analyze the importance of the POI area to users based on their movement patterns, thereby enhancing recommendation accuracy. The experimental results demonstrate that the proposed method outperforms other methods and significantly enhances the performance of recommendations. Originality/value The proposed POI recommendation integrates review analysis with geographic area analysis. Multiple features are extracted from user reviews, movement tracks, and geographic areas to represent user preferences and POI features. Feature extraction can effectively improve the accuracy of POI recommendations.