Web API recommendation method based on topic model clustering

Abstract

The invention discloses a Web API recommendation method based on topic model clustering. The method comprises the following steps: calculating semantic weight information of words according to context information so as to obtain a document-word semantic weight information matrix D; counting word co-occurrence information so that SPPMI matrix information is calculated; based on the obtained word frequency information matrix D of the words of the Mashup service document and the context SPPMI matrix M of the words, acquiring a word embedding information matrix by decomposing the M, combinding the two kinds of information , and calculating theme information of service; taking the obtained Mashup service theme features as spectral clustering input for clustering, segmenting a graph formed by all data points, wherein the sum of edge weights between different subgraphs after graph segmentation is made as low as possible, the sum of edge weights in the subgraphs is made as high as possible, and the clustering purpose is achieved; and combining GBDT and FM methods to predict and recommend the Web API service. Web API recommendation is effectively realized.

Description

technical field [0001] The invention relates to a method for constructing a Web API service recommendation based on theme model clustering for Mashup services. Background technique [0002] With the continuous maturity of Internet technology, the cost of Internet-based services continues to decrease. Driven by the idea of ​​"service-oriented" service computing, more and more companies publish data, resources or related businesses on the Internet to improve the utilization of information. rate and its own competitiveness. However, most of the traditional services follow the simple object access protocol, and usually provide a single-function service for the business needs of a specific field. In addition, there are problems such as complex technical system and poor scalability, which make it difficult to adapt to complex and changeable applications in real life. Scenes. Therefore, for Web service providers, how to quickly reintegrate existing service resources according to ...

AI Technical Summary

Problems solved by technology

[0004] However, as the number of Web API services on the Internet continues to increase, the recommendation system needs to face two problems in Web API recommendation: First, Mashup service description documents are usually short, with sparse features and less information. The short description information extracts the potential information of the Mashup service description requirements. Second, the large number of Web API service sets greatly increases the workload of the recommendation system to search for related API services. How to quickly and effectively determine the WebAPI service candidate set

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