Developing session-based personalized accommodation recommender system by using LSTM

Abstract

Tourism sector has been transformed by the advances in the Internet technology. Users can search for information and can select their destination from various alternatives by themselves, which brings the need for personal recommender methods. Personalized recommender system development is a complex topic. Demographic information, series of user clicks, and interactions and hotel features are examined to offer the appropriate set of hotels. Since the user interactions, clicks and hotel history is a time series data, Long Short-Term Memory models is a perfect fit to recommend a set of hotels from this data. In this study, we proposed a session-based accommodation recommender system that uses LSTM and achieved promising results.