Multi Cloud Based Service Recommendation System Using DBSCAN Algorithm
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Multi Cloud Based Service Recommendation System Using DBSCAN Algorithm K. Indira1 · M. K. Kavitha Devi2
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The historical collaborative filtering based recommendation system has become the most essential one in cloud computing environment. The recommender system which has been depends on collaborative filtering easily identify the user references and allow to learn relationship items—past users from the user group who exhibit similar preferences. Hence, the recommendation system has been considered as the most powerful tool for cloud providers and users. This paper proposed the clustering recommendation system executed in cloud environment. The accuracy of the system reduced when irrelevant features presents in data. So that in this proposed scheme, an effective feature selection approach named as modified LDA has been utilized for acquiring the relevant information only. The LDA technique defined as Linear Discriminant Analysis which decreased features numbers to expected value before classification process. DBSCAN is utilized as a clustering approach which provides better quality in terms of segregating the number of movies. Based on the genre, the similar movies are clustered together with the user ratings. DBSCAN elaborated as Density Based Spatial Clustering of Applications with Noise termed as famous method of learning or clustering which detached the high density clusters from low density clusters. The LDA technique has been utilized to accomplish the appropriate user reviews with categorized ratings. In this paper, the main challenge described as to analyze the reviews of the user with ratings using best clustering approach. The evaluation results proved the highest accuracy of 92.56% when compared with various methods of accuracy. This proposed method also has minimum execution time. Keywords DBSCAN density based · LDA · Recommender system · Clustering
* K. Indira [email protected] 1
Department of Information Technology, Thiagarajar College of Engineering, Madurai, India
2
Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, India
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Vol.:(0123456789)
K. Indira, M. K. Kavitha Devi
1 Introduction The cloud system provides various services and it’s difficult to choose the appropriate service. The cloud recommender system provides best item to choose for the cloud users based on intelligent engines. The cloud service platform presents the collaborative platform for cloud providers and cloud services to consumers. The collaborative filtering defines to create the powerful model for generating user recommendations. In cloud environment, the recommender system is the most important one for service based decision making. In recent years, recommendation services are enlarged with more attention and becomes more ubiquitous. In this personalized recommender systems, the list of items are made according to the active user’s behavior. The interaction among people and recommender sy
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