Making recommendations using transfer learning

Deep learning-based recommender systems have gained much attention due to the advantage of encoding content-based information, such as user textual reviews and item descriptions, images, or videos, without the trouble of manually crafting feature vectors. However, those systems are trained from scratch with randomly initialized parameters, where the training process can take a long time to converge. With the most recent breakthroughs in Natural Language Processing using transfer learning, pre-trained transformer-based models now provide a better foundation for textual information encoding. This inspires us to propose a transformer-based recommender system using transfer learning. As the first core contribution in this work, we apply transfer learning to the system, by fine-tuning the pre-trained transformer models for information encoding. The experiment result shows that the proposed system outperforms several other deep learning-based recommender systems on multiple datasets. As the second core contribution, we propose a novel user vector encoding algorithm that assists all the models to achieve a better performance, when the user content information is not available.