Bi-directional Encoder Representation of Transformer model for Sequential Music Recommender System

Abstract

A recommendation system is a set of programs that utilize different methodologies for relevant item selection for the user. In recent years deep neural networks have been used heavily for improving recommendation quality in every domain. We describe a model for music recommendation system that uses the BERT (Bidirectional Encoder Representations from Transformers) model. In the past, other deep neural networks have been used for music recommendation, which capture the the unidirectional sequential nature of a user’s data. Unlike other sequential techniques of recommendation, BERT uses bidirectional training of a user’s sequence for better recommendation. BERT uses the encoder part of the Transformer model, which uses an attention mechanism to learn contextual relations between a user’s past interactions. The proposed model relies on a user’s previous interaction to determine the bidirectional encoding for the model, which considers both the left and the right contexts. We evaluated our model with a baseline deep sequential model using two different datasets, and comparative results show that the model outperforms other sequential models.