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The rapid development of online services and information overload has inspired the fast development of recommender systems, among which collaborative filtering algorithms and model-based recommendation approaches are wildly exploited. For instance, matrix factorization (MF) demonstrated successful achievements and advantages in assisting internet users in finding interested information. These existing...
This paper studies the quality of web service prediction problem. We formalize the QoS prediction problem by incorporating multiple contextual characteristics via collective matrix factorization that simultaneously factor the user-service quality matrix and contextual information matrices. Using the service category and location context, we develop three context-aware QoS prediction models and algorithms...
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