Service recommendation method and system based on position and preference feature interaction

Abstract

The invention discloses a service recommendation method based on position and preference feature interaction. The method comprises the steps of obtaining identification information, position information and historical calling records of a user and a service; respectively obtaining a service set corresponding to the user and a user set corresponding to the service according to historical calling information and position information of the user and the service, and fusing and extracting interest preferences of the user and the service by utilizing a multi-head attention fusion network and an interest feature extraction network; after feature interaction is carried out on the interest preference, position information and identification information of the user and the service, the deep residual neural network is input, and a service quality prediction result is output; and selecting the service with the optimal service quality prediction result as the final recommendation service. According to the method, the problems of redundancy and inaccuracy of data are solved by positioning the interest preferences of the user and the service, the position information and the interest preferences of the user and the service are fused in the prediction of the service quality, linear and nonlinear transformation is performed on the features, and the prediction precision and the recommendation accuracy are improved.

Description

technical field [0001] The invention belongs to the technical field of intelligent recommendation, and in particular relates to a service recommendation method and system based on the interaction of location and preference features. Background technique [0002] With the rapid development of the Internet and the popularization of cloud computing and big data, people's quality of life has also improved, and people's demand for personalized recommendation services is increasing. How to accurately recommend personalized services for people has become a problem worth studying. . [0003] At present, Quality of Service (QoS) is a non-functional attribute of a service and an important criterion for evaluating service performance and utility. In the prior art, using QoS-based Web service recommendation technology to recommend the most suitable Web service for users has become a research hotspot in the field of service computing in recent years. By improving the accuracy of servic...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem of low accuracy of recommendation results in existing recommendation methods, the present invention provides a service recommendation method and system based on the interaction of location and preference features. interest preferences, solve the redundancy and inaccuracy problems caused by a large amount of data, and reflect the location preferences of users and services at the same time; integrate the interest preferences, location information and identification information of users and services into the neural network, It can better reflect the correlation between users and services; in order to better reflect the implicit relationship between users and services, use neural networks to perform deep feature interaction on features; complete accurate prediction of service quality through deep residual neural networks, so as to Realize the optimization of service recommendation results and improve the accuracy of service recommendation

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