Model-based collaborative filtering method for privacy protection collaborative Web quality-of-service prediction

Abstract

The invention discloses a model-based collaborative filtering method for privacy protection collaborative Web quality-of-service prediction. The method comprises the following steps of 1, performing data collection: locally collecting quality-of-service values, namely QoS values by users; 2, performing data disguise: disguising the QoS values; 3, performing model-based collaborative filtering on the disguised QoS values; and 4, predicting a result: predicting the result according to the collaboratively filtered QoS values. According to the method, differential privacy is introduced in a collaborative Web QoS prediction framework for the first time, so that the users can get maximum privacy protection by ensuring data availability. An experimental result shows that the method provides secure and accurate collaborative Web QoS prediction, and the model-based collaborative filtering method has superiority in the aspect of capturing a potential structure of QoS data.

Description

technical field [0001] The invention belongs to the field of computers, and in particular relates to a collaborative filtering method for privacy protection, in particular to a model-based collaborative filtering method for privacy protection and web service quality prediction. Background technique [0002] Quality of Service (QoS) is widely used to describe the non-functional characteristics of web services. Techniques for selecting, combining, and recommending web services based on quality of service have been extensively discussed in papers in recent years. The premise of these methods is that the precise QoS value of the Web service is always available. But obtaining precise QoS values ​​is not an easy task. On the one hand, the QoS values ​​published by service providers or third-party communities are inaccurate to service users because they are easily affected by uncertain Internet environments. On the other hand, due to time, cost, and other resource constraints, i...

AI Technical Summary

Problems solved by technology

However, another problem is that high-quality recommendations can only be achieved under weaker privacy parameters, or only for a small fraction of users.

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