# Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks
###### Abstract  
As an important branch of machine learning, recommendation algorithms have attracted the attention of many experts and scholars. The current recommendation algorithms all more or less have problems such as cold start and single recommended items. In order to overcome these problems and improve the accuracy of personalized recommendation algorithms, this paper proposes a recommendation for multi-task learning based on directed graph convolutional network (referred to as MTL-DGCNR) and applies it to recommended areas for e-commerce. First, the user's micro-behavior is constructed and converted into directed graph structure data for model embedding. It can fully consider the embedding of first-order proximity nodes and second-order proximity nodes, which can effectively enhance the transformation ability of features. Secondly, this model adopts the multi-task learning method, and uses knowledge graph embedding to effectively deal with the one-to-many or many-to-many relationship between users and commodities. Finally, it is verified by experiments that MTL-DGCNR has a higher interpretability and accuracy in the field of e-commerce recommendation than other recommendation models. The ranking evaluation experiments, various training methods comparison experiments, and controlling parameter experiments are designed from multiple perspectives to verify the rationality of MTL-DGCNR. recommendation algorithm; directed graph convolutional network; soft attention mechanism; multi-task learning; knowledge graph embedding +
Footnote †: journal: Medical Image Analysis  
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Footnote 1: email: [EMAIL_ADDRESS]  
Footnote †: journal: Medical Image Analysis  
Reference [14] fused the recommendation algorithm based on collaborative filtering and the recommendation algorithm based on content. Reference [15] used multiple models to calculate the features of users or commodities, respectively, and fused the features. In recent years, more researchers have paid attention to personalized recommendation technology based on user sequence behavior. [PERSON] et al. [16] proposed the Item2 vec method based on the user's click sequence information selected by the user. The Item2 vec method is concise and effective, but it only examines the local information of the ordering and cannot examine the overall effect of the user behavior sequence on the context. In order to make up for this deficiency, Session-based Recommendation Systems [17] have emerged quietly and have received extensive attention. [PERSON] et al. [18] proposed a robust method based on dynamic feature weight selection to realize the complex scenes. The other methods are also proposed to realize recommendation technology [19; 20; 21; 22; 23; 24]. With the rapid development of deep learning technology and powerful representation capabilities, many conversational recommendation algorithms based on deep learning have been born. In the use of deep learning to deal with the ranking problem, [PERSON] et al. [25] first proposed to apply the recurrent neural network (RNN) to the recommendation of established sequences and proposed the GRU4 Rec model. Compared with the traditional solution sequence method, this kind of model has a significant improvement in effect, but it only models the single-item transfer relationship between two proximity items, ignoring the problem of other item information in the session. Long short-term memory (LSTM) and gated recurrent units (GRU) networks [26] are often combined with matrix factorization methods to input user sequences into the network, and the user's preference is represented by the hidden state of the network. [PERSON] et al. [27] provided a set of deep interest network (DIN) training modes. DIN not only provides an adaptive understanding of customer preferences from historical activities to specific advertisements through a local activation module, and also provides two practical techniques of mini-batch-aware regularization and statistical adaptive trigger function to assist in training tens of thousands of network parameters.