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Home / programming language / Online Java Projects / Collaborative Filter ing Ser vice Recommendation Based on a Novel Similar ity Computation Method
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Collaborative Filter ing Ser vice Recommendation Based on a Novel Similar ity Computation Method

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SKU: PROJ8096 Categories: 2018-2019 Projects, Datamining Projects, Online Java Projects Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Matlab Project, PHP Project, Students Projects
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Collaborative Filtering  Service Recommendation Based on a Novel Similar ity Computation Method

Abstract-Recently, collaborative filtering-based methods are widely used for service recommendation. QoS attribute valuebased collaborative filtering service recommendation includes two important steps. One is the similarity computation, and the other is the prediction for the QoS attribute value, which the user has not experienced. In some previous studies, the similarity computation methods and prediction methods are not accurate. The performances of some methods need to be improved. In this paper, we propose a ratio-based method to calculate the similarity.

We can get the similarity between users or between items by comparing the attribute values directly. Based on our similarity computation method, we propose a new method to predict the unknown value. By comparing the values of a similar service and the current service that are invoked by common users, we can obtain the final prediction result. The performance of the proposed method is evaluated through a large data set of real web services. Experimental results show that our method obtains better prediction precision, lower mean absolute error ( M AE) and faster computation time than various reference schemes considered.

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