qos-based web service recommendation using popular-dependent collaborative filtering

Since most organizations present their services electronically, the number of functionally-equivalent web services is increasing as well as the number of users that employ those web services. consequently, plenty of information is generated by the users that leads the users to be in trouble with finding their appropriate web services. therefore, it is required to provide a recommendation method for predicting the quality of web services (qoss) and recommending web services. most of the existing collaborative filtering approaches do not operate efficiently in recommending web services due to ignoring some effective factors such as the dependency among users/web services, popularity of users/web services, and location of web services/users. in this paper, a web service recommendation method called popular-dependent collaborative filtering (pdcf) is proposed. the proposed method handles the qos differences experienced by the users as well as the dependency among users on a specific web service using the user/web service dependency factor. additionally, the user/web service popularity factor is considered in pdcf which significantly enhances its effectiveness. we also propose a location-aware method called lpdcf, which considers the location of web services into the recommendation process of pdcf. a set of experiments is conducted to evaluate the performance of pdcf and investigate the impression of the matrix factorization model on the efficiency of pdcf with two real-world datasets. the results obtained indicate that pdcf outperforms other competing methods in most cases.