Web service applications and consumer environments based on ICT-driven optimization

Networking is the use of physical links to connect individual isolated workstations or hosts together to form data links for the purpose of resource sharing and communication. In the field of web service application and consumer environment optimization, it has been shown that the introduction of network embedding methods can effectively alleviate the problems such as data sparsity in the recommendation process. However, existing network embedding methods mostly target a specific structure of network and do not collaborate with multiple relational networks from the root. Therefore, this paper proposes a service recommendation model based on the hybrid embedding of multiple networks and designs a multinetwork hybrid embedding recommendation algorithm. First, the user social relationship network and the user service heterogeneous information network are constructed; then, the embedding vectors of users and services in the same vector space are obtained through multinetwork hybrid embedding learning; finally, the representation vectors of users and services are applied to recommend services to target users. To verify the effectiveness of this paper’s method, a comparative analysis is conducted with a variety of representative service recommendation methods on three publicly available datasets, and the experimental results demonstrate that this paper’s multinetwork hybrid embedding method can effectively collaborate with multirelationship networks to improve service recommendation quality, in terms of recommendation efficiency and accuracy.

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