MDAR: A Knowledge-Graph-Enhanced Multi-Task Recommendation System Based on a DeepAFM and a Relation-Fused Multi-Gead Graph Attention Network

In recent years, MKR has attracted increasing attention due to its ability to enhance the accuracy of recommendation systems through cooperation between the RS tasks and the KGE tasks, allowing for complementarity of the information. However, there are still three challenging issues: historical behavior preferences, missing data, and knowledge graph completion. To tackle these challenging problems, we propose MDAR, a multi-task learning approach that combines DeepFM with an attention mechanism (DeepAFM) and a relation-fused multi-head graph attention network (RMGAT). Firstly, we propose to leverage the attention mechanism in the DeepAFM to distinguish the importance of different features for target prediction by assigning different weights to different interaction features of the user and the item, which solves the first problem. Secondly, we introduce deep neural networks (DNNs) to extract the deep semantic information in the cross-compressed units by obtaining the high-dimensional features of the interactions between the RS task and the KG task to solve the second problem. Lastly, we design a multi-head graph attention network for relationship fusion (RMGAT) in the KGE task, which learns entity representations through the different contributions of the neighbors by aggregating the relationships into the attention network of the knowledge graph and by obtaining information about the neighbors with different importance for different relationships, effectively solving the third problem. Through experimenting on real-world public datasets, we demonstrate that MDAR obtained substantial results over state-of-the-art baselines for recommendations for movie, book, and music datasets. Our results underscore the effectiveness of MDAR and its potential to advance recommendation systems in various domains.