The effective selection and recommendation techniques are needed for increasing presence and adaption of web services in the real world. The collaborative filtering method collects the similar data from other web services and it predicts the current user value. The main assumption of CF is that the users and items have similar behaviors, they will rate or act on other items similarly. Collaborative filtering approach have many challenges, CF algorithms are required highly sparse data. A user collaborative mechanism is used to collect past web service QoS information from different service users. Then based on the collected QoS data a collaborative filtering approach is designed to predict web service QoS values.
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