Random walk and diversity graph sorting-based personalized service recommendation method

Abstract

The invention relates to a random walk and diversity graph sorting-based personalized service recommendation method. Firstly the defect in calculating user similarity by a conventional Pearson correlation coefficient is analyzed, and then a similarity relationship between users is subjected to multi-time transmission through a WRW model, so that more similar neighbors are found for a target user,and the data sparsity problem is effectively solved; based on predicted QoS values of all the similar neighbors, a service graph model is built by adopting an SGMC algorithm to filter a large amount of candidate services with excessively low performance, reduce an algorithm optimization space, and ensure quick convergence of a subsequent diversity graph sorting algorithm; and finally, an optimal node set is searched for on the service graph model by adopting an ONCS policy, and k nodes contained in the set are a service list recommended to the user and having recommendation accuracy and functional diversity. The method has relatively high recommendation accuracy and relatively good functional diversity, and can meet potential functional demands of the users to the maximum extent while ensuring service recommendation quality.

Description

technical field [0001] The invention belongs to the field of Internet service computing, in particular to a personalized service recommendation method based on random walk and diversity graph sorting. Background technique [0002] With the further promotion of the cloud computing model, the flexible cloud deployment model and service payment model have given birth to various types of cloud services. Faced with a large number of candidate services with similar functions but different Quality of Service (QoS), it is difficult for users to select the best service that meets their needs based on their own limited knowledge and experience. In this context, service recommendation technology has become an important means to solve the user's service selection dilemma, and has received more and more attention in the field of service computing in recent years. [0003] Collaborative filtering is the most widely used important technology in the recommendation system. Based on the assu...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the deficiencies in the prior art, the present invention provides a personalized service recommendation method based on random walk and diversity graph sorting, which solves the problem of low recommendation accuracy and recommendation failure caused by problems such as data sparsity in traditional service recommendation. The result is lack of diversity and other defects. Based on the analysis of the sparseness of direct similar relationships, a weighted random walk model is proposed to mine more similar relationships by performing random walks on the user network. Based on all similar user prediction services QoS value, and build a service graph model to filter a large number of candidate services with low performance; combined with the optimal node set selection strategy, use the greedy algorithm to obtain a service recommendation list with both recommendation accuracy and functional diversity, so that it can satisfy users It can effectively balance the relationship between service recommendation accuracy and functional diversity to achieve the best personalized recommendation effect.

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