Effective and Scalable Recommendation Model Combining Association Rule Mining and Collaborative Filtering In Big Data
Manikandan R1, Ramesh R2, Saravanan V3
1Manikandan R , College of Enginering , Trivandrum , (Kerala), India
2Ramesh R , Computer Science, Sri Krishna College of Arts and Science Coimbatore, (Tamil Nadu), India.
3Saravanan V, Computer Applications, Sri Venkateswara College of Computer Applications and Management, Coimbatore, (Tamil Nadu), India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 929-931 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2548037619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Due to the huge volume of information over the internet, The process of retrieving apt information is becoming more and more challenging. Many researchers have been carried out to sort this issue and the recent ones include Recommender Systems that are intelligent enough to predict the apt information and web pages that an user is anticipating. Collaborative Filtering is the well known method of any Recommendation model but the it has major drawbacks such as scalability and accuracy. The presented work is intended to combine the CF and association rule mining which is generically used for Big data, The aim of the research is to give a Recommendation model that is more scalable and accurate. We have taken the personalized e-book recommendation model that takes the previous users’ browsing pattern.
Keywords: Recommendation Collaborative filtering Association rule mining, Big data
Scope of the Article: Data Analytics