Enhancing POI recommendations on social media: a sequential approach incorporating LSTM and user feedback

As location-based social media has rapidly grown, trip recommendations have become increasingly important. Many recommender systems do not consider the valuable information contained in user reviews, which is a missed opportunity. By incorporating review text, recommendation performance can be improved, and the Cold Start issue could be alleviated. This study proposes a new personalized method for recommending Point of Interest (POI) trips based on user reviews. The proposed method reduces the time required to find POIs using two-level clustering based on the Manhattan interval. Furthermore, our method employs an LSTM network (Long Short-Term Memory) to find similar users based on their feedback, reducing data scarcity's impact and coping with the Cold Start issue. Moreover, it introduces multifaceted contextual information and represents a novel approach to determining user preferences. Finally, this neural hybrid framework identifies a list of the most efficient trip candidates by mining personalized POIs in a sequential pattern and incorporating them into the recommendation process. The proposed methodology was tested using datasets from Yelp, Gowalla, and Tripadvisor, and the results represented that it performed better than other methods in multiple metrics, including MAP, NDCG, RMSE, and F-Score.