Cross attention fusion for knowledge graph optimized recommendation

Knowledge Graph has attracted a wide range of attention in the field of recommendation, which is usually applied as auxiliary information to solve the problem of data sparsity. However, most recommendation models cannot effectively mine the associations between the items to be recommended and the entities in the Knowledge Graph. In this paper, we propose CAKR, a knowledge graph recommendation method based on the cross attention unit, which is similar to MKR, a multi-task feature learning general framework that uses knowledge graph embedding tasks to assist recommendation tasks. Specifically, we design a new method to optimize the feature interaction between the items and the corresponding entities in the Knowledge Graph and propose a feature cross-unit combined with the attention mechanism to enhance the recommendation effect. Through extensive experiments on the public datasets of movies, books, and music, we prove that CAKR is better than MKR and other knowledge graph recommendation methods so that the new feature cross-unit designed in this paper is effective in improving the accuracy of the recommendation system.