# Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks
###### Abstract
An attention mechanism is applied in a neural network as an efficient auxiliary module to enhance the interpretability of the model. [PERSON] et al. [28] improved DIN and established a deep interest evolution network (DIEN) model. By adding an interest extraction layer to the network and by adding an attention mechanism in the interest evolution layer to capture the latest evolution process related to the target item, the real-time preference of the user was captured in the sequence feature representation of the target user. [PERSON] et al. [29] proposed a recommendation model SASRec based on a sub-attention mechanism. SASRec can not only capture the long-term semantics of users like the RNN model, but also achieves recommendation with relatively few operations. [PERSON] et al. [30] propose an item-level and component-level attention mechanism in the field of multimedia implicit feedback. [PERSON] et al. [31] proposed an attention mechanism to learn the importance of each neighbor node, and the collaborative filtering signal was obtained by using the attention score to improve the recommendation accuracy. [PERSON] et al. [32] proposed a Transformers4 Rec model, which also achieved excellent performance in the field of e-commerce based on the transformer architecture. [PERSON] et al. [33] proposed an approach to infer user contextual recommendations from the dynamics of social tag embeddings of music. [PERSON] et al. [34] proposed a short-session next-item recommendation based on a session-based recommendation system (SBRS). In addition, some other deep learning models are proposed, which can be applied in the recommendation [35; 36; 37; 38; 39; 40]. In practical applications, many data are graph-structured, such as knowledge graphs, social networks, molecular networks, etc., followed by the emergence of graph neural networks (GNNs). [PERSON] et al. [41] firstly applied graph convolution network (GCN) to the contact graph of visits between users and items, and proposed a GC-MC model. [PERSON] et al. [42] deeply researched and summarized the application of graph neural networks in recommendation systems. The IGPL algorithm [43] transforms the score prediction problem into link prediction by extracting the partially closed sub-graphs of the users and items and inputting the sub-graphs into the graph neural network for training. It not only enhances the explanatory power of the model, but also learns the deep features of each node. [PERSON] et al. [44] proposed a session-based recommendation with graph neural networks (SR-GNN), which models all user sequences through directed graphs, and then uses GNN to learn each the hidden vector representation of the commodity. Then, the embedding of each session is obtained through the attention architecture model. Finally, the global prediction is achieved through the softmax layer. [PERSON] et al. [45] proposed a content-rich graph neural network with attention (CoRGi). It used an attention mechanism. This algorithm considers the contextual nodes of node neighbors and gives a personalized attention mechanism as a message delivery. Experiments show that this method is better at predicting edge values in sparse regions of the graph. The above recommendation algorithms all have the defects of cold start and single recommendation items, more or less. In order to solve the problem of only modeling the single-item transfer relationship between two proximity items, ignoring the information of other items in the session; in order to effectively deal with the one-to-many or many-to-many relationship between users and items, obtain a clearer session representation and reduce the cold start problem; in order to effectively enhance the changing ability of features, a simpler feature distribution than GCN is obtained. This paper takes advantage of a directed graph convolutional network, a soft attention mechanism, and multi-task learning, integrates their related technologies, and proposes a recommendation for multi-task learning based on directed graph convolutional network (MTL-DGCNR).