Accommodating qos prediction
Since many mobile services have been or being developed as the interfaces to access resources on mobile environment, the number of services increases dramatically.Users often have to select a mobile service from a series of service candidates with similar function.To solve the selection issue, people develop the service recommender system to select services with better Qo S (short for quality of service).
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.
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.
So it is difficult to conduct similar neighbor identification with high accuracy in sparse Qo S records.
So we decide to propose new models that can handle the above issues, and our models are based on Slope One model.