Call For Paper Volume:4 Issue:7 Jul'2017 |

Clustering Based Collaborative Filtering (CluBCF) Using HACE Theorem for Big Data

Publication Date : 31/03/2015

Author(s) :

Suguna. G , Karthik. R.

Volume/Issue :
Volume 2
Issue 3
(03 - 2015)

Abstract :

The Big Data is a wider technology that is used to describe massive volume of both structured and unstructured data. It has the potential ability to help companies that are improve operations and make faster, more intelligent decisions for every problem. Recommender Systems (RS) is a concept of providing personalized recommendation from large number of items. Many algorithms are available that are used to provide recommendations using ratings. One of such famous algorithms is collaborative clustering. A CluBCF (Clustering-Based Collaborative Filtering) is a new approach it has a service and its aims at gathering similar services in the same clusters to the recommend services collaboratively. It has two stages. First stage, the services are divided into small-scale cluster and the second stage, a collaborative filtering algorithm is imposed on one of the clusters. On the filtering stage the HACE (Heterogeneous Autonomous Complex Evolving) theorem is to be used. It suggests that the key characteristics of the Big Data are 1) huge with heterogeneous and diverse data sources, 2) autonomous with distributed and decentralized control, and 3) complex and evolving in data and knowledge associations. The HACE theorem is a data-driven model and it involves demand-driven aggregation of information sources, mining and analysis, user interest modeling and security and privacy considerations. At the filtering stage HACE theorem is to be applied for further process. 

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Clustering Based Collaborative Filtering (CluBCF) Using HACE Theorem for Big Data

March 11, 2015