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A Location and Reputation Aware Matrix Factorization Approach for Personalized Quality of Service Prediction

Prediction of Quality of Service (QoS) values plays an important role in service selection, discovery, and recommendation. Previous works show that the QoS values would be influenced by the location information. However, these researches do not consider the fact that some users may provide untrustworthy QoS values even though they are in the same location region. QoS values from these unreliable users could significantly affect the QoS prediction accuracy. To address this issue, this paper proposes an alternative and efficient approach to predict the missing QoS values, referred as the Location and Reputation aware Matrix Factorization based Location Information (LRMF). LRMF combines both the user's reputation and location information to achieve more accurate prediction results. Experiments are conducted on a real-world Web service QoS dataset, and results show that the proposed method outperforms many other existing QoS prediction methods.