Improved session-based recommender systems using curriculum learning

Abstract

Curriculum Learning (CL) is an effective technique to train machine learning models where the training samples are supplied to the model in an easy-to-hard manner. Similar to human learning, the model can benefit if the data is given in a relevant order. Based on this notion, we propose to apply the concept of CL to the task of session-based recommender systems. Recurrent Neural Networks and transformer-based models have been successfully utilized for this task and shown to be very effective. In these approaches, all training examples are supplied to the model in every iteration and treated equally. However, the difficulty of a training example can vary greatly and the recommendation model can learn better if the data is given according to an easy-to-difficult curriculum. We design various curriculum strategies and show that applying the proposed CL techniques to a given recommendation model helps to improve performance.