Leveraging the capsule network to learn content text for collaborative filtering
Ji Li, Suhua Wang · International Journal of Communication Networks and Distributed Systems · 2023
At present, most of the building components, technologies and frameworks of deep learning are based on convolutional networks. However, some deep learning studies on image processing have shown that the capsule network can be more representational because it can capture various 'posture' changes, including translation, rotation and scaling, and can remember the position relationship between parts. Despite the intriguing nature of the capsule network and its potential to open up entirely new natural language processing architectures, little work has been done in this area. In this work, we use the capsule network to learn the content text of the item (such as the plot text of the movie or the description document of the product), to obtain a better representation of the item and help achieve a more accurate recommendation. We proposed 'leveraging the capsule network to learn content text for collaborative filtering (CCCF)'. This model combines the capsule network and neural matrix factorisation to effectively model text data and user-item ratings. Experiments conducted from different perspectives on two popular datasets show that CCCF achieves good performance in common recommendation tasks, which proves the effectiveness of the capsule network in recommendation.