In this paper we discussed conventional approaches to building sequential recommender systems and implemented BERT4Rec and LSTM-based models for two type of tasks in sequential recommendation: Relevance Prediction and Next Item Prediction. The usage of these models allowed to make use of sequential context, that is extremely important to simultaneously model the short-term interest of users in the session. Moreover, the usage of these models resulted in overall 0.5% increase in the quality of recommendations.