Session-based recommender system refers to a specific type of recommender system that focuses more on the transactional structure of each session rather than the user and item interactions . It is stated that the users' interactions are mostly homogeneous in the same sessions, while being heterogeneous across different sessions . Therefore, it is essential to extract the interest dynamics of users within each session. The 2019 ACM Recsys Challenge  aims to apply session-based recommender systems to the domain of travel metasearch. The goal is to predict which hotels are clicked in the search results based on the context of each session. In this paper, we propose our approach to effectively tackle the challenge. It involves an ensemble of three models, LightGBM, XGBoost, and a Neural Network based on DeepFM  that is capable of handling sequential features. Our team, RosettaAI, won the 4th place in this challenge, scoring 0.679933 on the final leaderboard.