Method for recommending context-aware Web service on basis of weighted time-space effects

Abstract

The invention discloses a method for recommending context-aware Web service on the basis of weighted time-space effects. The method includes building weighted time decay models so as to find user sets with preference similar to preference of current users under condition that time decay effects are considered; acquiring user sets in contexts similar to current contexts of the users by the aid of location-aware similarity mining algorithms; building relation models of time-space correlation and user preference so as to obtain network service call records conforming to the current preference of the users; predicting QoS (quality of service) values of service by the aid of Bayes theorems on the basis of the obtained user sets with the preference similar to the preference of the current users under the condition that the time decay effects are considered, the user sets in the contexts similar to the current contexts of the users and the network service call records conforming to the current preference of the users so as to obtain the service with the highest degrees of satisfaction; evaluating outcome of the obtained service. The method has the advantage that the recommendation accuracy can be improved by the aid of the method.

Description

technical field [0001] The invention relates to the Internet field, in particular to a context-aware Web service recommendation method based on weighted spatio-temporal effects. Background technique [0002] Web service is a service-oriented application program, which provides services through standard Web protocols, in order to ensure that application services on different platforms can achieve interoperability. Common Web services include those that provide weather forecast queries, those that provide application downloads, and so on. With the rapid development of network technology, a large number of Web services with similar functions are released on the Internet. Therefore, how to filter and recommend Web services for users to meet their individual needs is an urgent problem to be solved. Aiming at this, industry and academia have done a lot of research and practice on Web service recommendation from multiple perspectives, and proposed various technologies and systems...

AI Technical Summary

Problems solved by technology

The disadvantage of this technical solution is that there are huge differences in the historical ratings of different Web services by users, but this method ignores the different impacts of differentiated ratings on Web service recommendations, so the recommendation algorithm is inefficient in the similarity calculation process. defect

Disadvantages: The QoS value (such as response time) of Web services depends largely on situational factors such as network conditions

The disadvantage of the above method is that the spatial position of the user will change with time, but the existing technology rarely considers the influence of spatiotemporal correlation on user preference

[0010] The disadvantages of the above three solutions are: first, the time decay effect is considered in the process of collaborative filtering, but the impact of different QoS values ​​on collaborative filtering with different weights is not considered, that is, the consideration of the weighted time decay effect is lacking; Second, ignoring the current situational information of the user, the QoS value of Web services (such as response time) largely depends on situational factors such as network conditions

However, this method lacks consideration of the context similarity effect

Therefore, the defect of low QoS prediction accuracy is caused; third, only the context information is used as a "filter", that is, the historical invocation data related to the current user preference obtained by screening is used for recommendation, but because the temporal and spatial correlation is not considered The influence between user preferences, thus affecting the accuracy of the recommendation method

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