Research on Service Recommendation Method of Multi-network Hybrid Embed-ding Learning

The network embedding method can map the network nodes to a low-dimensional vector space and ext-ract the feature information of each node effectively. In the field of service recommendation, some studies show that the introduction of network embedding method can effectively alleviate the problem of data sparsity in the recom-mendation process. However, the existing network embedding methods are mostly aimed at a specific structure of the network, and do not cooperate with a variety of relationship networks from the source. Therefore, this paper proposes a service recommendation method based on multi-network hybrid embedding (MNHER), which maps mul-tiple relational networks to the same vector space from vertical and parallel perspectives. Firstly, the social network of users, the shared network of service tags and the user-service heterogeneous information network are constructed. Then, the hybrid embedding method proposed in this paper is used to obtain the embedding vector of users and services in the same vector space. Finally, the service recommendation is made to target users based on the embed-ding vector of users and services. In this paper, the random walk method is further optimized to extract and retain the characteristic information of the original network more effectively. In order to verify the effectiveness of the method proposed in this paper, it is compared with a variety of representative service recommendation methods on three public datasets, and the F-measure values of the service recommendation methods based on single relational network and simply fused multi-relational network are improved by 21% and 15%, respectively. It is proven that the method of multi-network hybrid embedding can effectively coordinate multi-relationship network and improve the quality of service recommendation