Research On Neural Recommendation Based On Graph Structure Learning

Internet companies hope to make their products appeal to users' interests and improve their stickiness through precise information pushing strategies.Meanwhile,users face the problem of information overload and expect to see the information they are really interested in.As an effective solution to the information overload problem,recommender systems have received a lot of attention from both academia and industry in recent years.This thesis has studied the research on neural recommendation based on graph structure learning,abstracting the ”user-purchase-item” event group in the recommendation problem into the form of ”node-edge-node” on bipartite graphs,and performing feature learning and recommendation on the corresponding graph structure.Specifically,the study coverd following three aspects.(1)Propose a graph collaborative filtering algorithm combined with graph embedding based pre-training: the core idea of this scheme is to propose the use of a graph embedding algorithm to pre-train the node embedding vectors on bipartite graphs,and then learn the node vector representation by the proposed graph collaborative filtering model.The graph embedding algorithm is able to extract the higher-order structural properties of the nodes to enrich the node representation;the graph collaborative filtering model uses the connectivity of the graph to learn the low-order collaborative information of the nodes to obtain the final representation vectors of the nodes.Experiments show that the method can effectively improve the representation of nodes and enhance the recommendation performance of the model.(2)Propose a group-based graph collaborative filtering model(GGCF): compared with the previous recommendation models on bipartite graphs,GGCF not only considers the nodes' self-features when learning node representations,but also involves the nodes' group information as part of the nodes' features.Specifically,firstly,nodes are divided into different groups based on random walk,and then the group information is vectorized and participated in the training of graph collaborative filtering model as part of node features;in the graph collaborative filtering model,the information weights from different neighbors are calculated by the self-attention mechanism to achieve better node representation updates.Experiments show that better node representation and better recommendation performance can be obtained by this method.(3)Propose a group based multilayer perceptron collaborative filtering model(GMCF):compared with the GGCF model,the GMCF model uses a multilayer perceptron structure to train the training data pairwise while using the group information,which effectively solves the problem of high computational resource consumption in GGCF;in the acquisition of node group features,the attention mechanism is used to aggregate the information from different group.In addition,the interaction characteristics between nodes are also added to the node features.The experiments show that GMCF can obtain better performance of expression vectors and achieve better recommendation performance than the traditional method of learning node expressions using only the node features.In summary,this thesis improves and innovates on the recommendation problem on bipartite graphs.Due to the universality of the bipartite graph structure,the proposed algorithmic model can be applied to most recommendation scenarios,which has certain reference value and positive significance to alleviate the current information overload problem.