An Incremental-And-Static-Combined Scheme for Matrix-Factorization-Based Collaborative Filtering
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 1
Abstract
The last decade has witnessed a tremendous growth of Web services as a major technology for sharing data, computing resources, and programs on the Web. With increasing adoption and presence of Web services, designing novel approaches for efficient and effective Web service recommendation has become of paramount importance. In existing web services discovery and recommendation approaches focus on keyword-dominant Web service search engines, which possess many limitations such as poor recommendation performance and heavy dependence on correct and complex queries from users. Recent research efforts on Web service recommendation center on two prominent approaches: collaborative filtering and content-based recommendation. Unfortunately, both approaches have some drawbacks, which restrict their applicability in Web service recommendation. In proposed system for recommendation we will be using Agglomerative Hierarchal Clustering or Hierarchal Agglomerative Clustering for effective recommendation in web-services. our approach considers simultaneously both rating data (e.g., QoS) and semantic content data (e.g., functionalities) of Web services using a probabilistic generative model.
Authors and Affiliations
K. P. Ashvitha, B. Jerlyn Flora, S. Apoorva, D. Lakshmi
Keywords
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- EP ID EP23070
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