Web API recommendation method based on topic model clustering

Abstract

A Web API recommendation method based on topic model clustering, calculates the semantic weight information of words according to context information to obtain document-word semantic weight information matrix D; counts word co-occurrence information to calculate SPPMI matrix information; obtains Mashup service based on The word frequency information matrix D of the document word, the context SPPMI matrix M of the word, the word embedding information matrix can be obtained by decomposing M, and the above two kinds of information can be further combined to calculate the topic information of the service; the obtained topic feature of the Mashup service is used as the spectrum The input of the clustering is clustered. By cutting the graph composed of all data points, the edge weights between different subgraphs after the graph are cut as low as possible, and the edge weights in the subgraphs are as high as possible. The purpose of clustering; combining GBDT and FM methods to make prediction recommendations for Web API services. The present invention effectively realizes Web API recommendation.

Description

technical field [0001] The invention relates to a Web API service recommendation method based on topic model clustering constructed for Mashup service. 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-based" service computing, more and more companies publish data, resources or related services on the Internet to improve the utilization of information. rate and 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 are 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 actual users and specific...

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 Web API service candidate set

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