Accommodating qos prediction

11-Mar-2020 11:58 by 8 Comments

Accommodating qos prediction

However, the Slope One model also has a defect; that is, the model needs to use all of the known Qo S records for a missing value prediction.

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But in mobile service invocation, most users only have invoked quite a few services before, and a large part of Qo S values are unknown.

We also build an ensemble model, which combines the prediction results of FB-CF model and FB-MF model.

We conduct sufficient experiments, and the experimental results demonstrate that our models outperform all compared methods and achieve good results in high data sparsity scenario.

With the increase of the number of mobile services, for service recommendation and selection, the nonfunctional properties (also known as quality of service, Qo S) become increasingly important.

However, in many cases, the number of mobile services invoked by a user is quite limited, which leads to the large number of missing Qo S values.

In mobile service recommendation, the accuracy of similar neighbor identification is not so well due to the following reasons: In similar neighbor identification, there is an assumption that the Qo S values are stable and reliable.

However, Qo S is largely impacted by the mobile network environment in different locations, both in the user side and service side.The proposed data filtering strategy is not designed to any specific Qo S property of a mobile service but can be used to predict all types of Qo S properties.It conducts sufficient experiments in two real-world datasets, and the experimental results demonstrate the effectiveness of the proposed models.In high data sparsity, the number of services invoked by a single user is quite limited, which leads to the even few number of common invoked mobile services by more than one user.Especially in the extreme case that two users do not have any services commonly invoked, there is no chance for any two users being the similar neighbor of the other.On the other hand, it is inevitable to involve noise data, which lowers the prediction accuracy.

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