Personalized service recommendation system and method based on latent semantic probability models

Abstract

The invention relates to a personalized service recommendation system and method based on a latent semantic probability models and belongs to the technical field of service computing. The method includes: determining a QoS (quality of service) index system for evaluating performance of a series of services with similar functions; building latent semantic probability models among users, user index preferences and service situations; collecting index preference information provided by user-system interaction when different users use services with different functions under different service situations in a system, and saving the information in a historical experience database; using collected data to train parameters of the latent semantic probability models; when a user is unfamiliar with a certain service situation and needs to call a service with a special function under the service situation, using the trained latent semantic probability models to predict index preference of the user; comprehensively screening candidate services according to the predicted personalized QoS index preference, selecting the service most suitable for the user's requirements, and personalized service recommendation is achieved.

Description

technical field [0001] The invention belongs to the technical field of service computing, and in particular relates to a personalized service recommendation system and method based on a hidden semantic probability model. Background technique [0002] With the rapid development of Internet technology, service computing technology has been widely used. Web service is such a loosely coupled software system that runs on the Internet and supports interoperability between different platforms. The "binding" pattern allows for a loosely bound relationship between service consumers and providers, which lays the foundation for service usage. However, the separation of users and providers of web services increases the difficulty for service users to understand services. At the same time, as the number of web services running on the Internet continues to increase, service users need to choose from many services with similar functions. It is undoubtedly a heavy task for most service use...

AI Technical Summary

Problems solved by technology

[0007] The above methods all involve personalized service recommendation, but few services are screened for the non-functional attributes of web services to meet the individual needs of users. The non-functional attributes of services are often the main manifestation of service performance, so how to use the service Non-functional attributes objectively evaluate the performance of services while meeting the individual needs of different users is a problem to be solved for service recommendation

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