KNCR: Knowledge-Aware Neural Collaborative Ranking for Recommender Systems

The recommendation system is designed to generate a personalized sorting list of items that users may be interested in. With the unprecedented success of deep learning in the field of Computer Vision and Voice recognition, how to reasonably introduce deep learning into the recommendation system has also aroused the thinking of researchers. Knowledge graph, as a new research hotspot, contains abundant new auxiliary information of entity semantic association. The researchers found that when the knowledge map is introduced into the recommendation system, it can reduce the data sparsity and cold start problem, and it is a good assistant for neural network in the recommendation system. In the traditional recommendation system, because it relies on the matrix decomposition and collaborative filtering algorithm for recommendation, there will inevitably be problems of cold start and data sparsity. The problem of data sparsity often refers to the large number of users and items in platforms such as large-scale e-commerce, but in the user-item matrix obtained, the average number of users interacting with the project is small, which will cause the user-item matrix to be sparse. The cold start problem refers to how to make personalized recommendation for new users without a large number of user data. The sparsity of data will eventually lead to the inability to capture the relationship between different users and different items, thus reducing the accuracy of the recommendation system. As an implicit expression, implicit feedback can get users' preferences in many ways, rather than limited to the display of expression preferences, so as to enrich the user-item matrix and alleviate the problem of data sparsity. Neural network can analyze the relationship between things from a higher dimension, and improve the data sparsity. The knowledge graph contains the fact relationship of a thing in the real world, which is equivalent to providing additional information dimension for the data, so as to solve the cold start problem to a certain extent. In this paper, we propose an enhanced collaborative filtering recommendation algorithm based on implicit feedback and representation learning of the knowledge graph combined with neural network (KNCR). KNCR can bridge intrinsic relationship between items that are not considered by the traditional collaborative filtering algorithm, and effectively solve the problems of sparse scoring matrix and cold start. The experimental results from the real-world public dataset demonstrate that KNCR can improve the performance of personalized recommendations.