A sequential neural recommendation system exploiting BERT and LSTM on social media posts

Abstract

AbstractTourists share opinions about Points of Interest (POIs) through online posts and social media platforms. Opinion mining is a popular technique for extracting feedback from tourists who visited various places hidden in reviews, which are used in several tourist applications that generally reflect their preference towards POI. On the other hand, a trip schema is difficult for tourists because they must pick up sequential POIs in unknown areas that meet their limitations and preferences. However, most prior trip suggestion methods are suboptimal for several reasons, including that they do not consider valuable user reviews and rely exclusively on left-to-right unidirectional discovery sequence models. This study proposes a Neural Network-Long Short-Term Memory (LSTM) POI recommendation system for calculating user similarity based on opinions and preferences. In addition, it presents a method for discovering sequential trip recommendations with Bidirectional Encoder Representations from Transformer (BERT) using a deep learning method. Furthermore, this neural hybrid framework identifies a list of optimal trip candidates by combining personalized POIs with multifaceted context. Furthermore, this method employs the valuable information contained in user posts and their demographic information on social media to mitigate the well-known cold start issue. In the experimental evaluation based on two datasets, Tripadvisor and Yelp, this hybrid method outperforms other state-of-the-art methods when considering F-Score, nDCG, RMSE, and MAP.