Service recommendation method based on trust extension and listwise rank learning

Abstract

The invention relates to a service recommendation method based on trust extension and listwise rank learning, which comprises the steps of firstly representing each user to be the probability distribution of invoked service set arrangement by utilizing service ranking position information and referring to a Plackett-Luce model, and calculating the probabilistic user similarity based on the KL distance; taking a direct trust relationship and an indirect trust relationship between the users into consideration at the same time, calculating the direct trust degree by using a Beta trust model, calculating the indirect trust degree by using transfer characteristics of the trust relationships, obtaining the comprehensive trust degree, and constructing a trusted neighbor set of target users; integrating the trusted neighbor set into a matrix decomposition model, taking a cross entropy between a predicted ranking list and a correct ranking list as a loss function, designing a listwise rank learning algorithm to obtain an optimal ranking model, and outputting a recommendation list which best conform to user interests. The service recommendation method has high recommendation accuracy, and can satisfy potential functional requirements of the users to be greatest extent while ensuring the service recommendation quality.

Description

Technical field [0001] The invention belongs to the field of Internet service computing, and particularly relates to a service recommendation method based on trust expansion and list-level ranking learning. Background technique [0002] With the popularization of the Internet and the rapid development of cloud computing technology, the web services provided on the Internet have grown exponentially, which constitutes an information explosion. Users urgently need an effective service recommendation method to solve their selection dilemma. Therefore, service recommendation technology has gained widespread attention in the field of service computing. The Quality of Services (QoS) of Web services includes service failure rate, response time, cost, throughput, etc. It is one of the important attributes that users need to consider when selecting services. Because Web services are widely distributed in the network, some QoS attributes such as response time and throughput are often affe...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the deficiencies in the prior art, the present invention provides a service recommendation method based on trust extension and list-level sorting learning, which solves the inaccuracy and sparsity of user trust relationships caused by ranking only based on QoS prediction values ​​in traditional service recommendation algorithms Questions and other issues, by representing the user as the probability distribution of the called service set, calculate the probabilistic user similarity based on the Kullback-Leibler distance; through the trust extension model, fully mine the user trust relationship, and combine the user similarity construction as the goal The user builds a set of trusted neighbors; and uses the set of trusted neighbors to improve the list-level sorting learning algorithm to train the optimal sorting model, so that the service recommendation list output by it is most in line with the interests and preferences of the target users, effectively reducing the complexity of service calculations. Improve the accuracy of service recommendations

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