A BERT-Based Sequential POI Recommender system in Social Media

Route schema is challenging for tourists because they must choose Points of Interest (POIs) in unknown areas that meet their preferences and limitations. Historically, sequential methods were utilized to generate recommendations based on previous user interactions. Despite their efficacy, however, such left-to-right unidirectional models are suboptimal due to the following factors: a) user behavior sequences are restricted in their ability to utilize hidden representations in unidirectional architectures; b) a rigidly ordered sequence is frequently assumed but is not always possible. This paper proposes a novel personalized sequential recommendation model, termed BERTSeqHybrid, which utilizes Bidirectional Encoder Representations from Transformers (BERT) to circumvent these limitations. In addition to contextual data from POIs, asymmetric schemas, and topic modeling are employed to improve the user-user similarity model. Furthermore, a novel method for evaluating user preferences is proposed utilizing explicit demographic data to mitigate the cold start problem. In the experimental evaluation, the developed methodology, which was applied to two different datasets (Yelp and Flickr), produced superior root mean square error RMSE, F-Score, mean average precision (MAP), and normalized discounted cumulative gain (NDCG) indexes.