Effective Hybrid Recommender Approach using Improved K-means And Similarity

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-36 Number-3
Year of Publication : 2016
Authors : Prerana Khurana, Shabnam Parveen
  10.14445/22312803/IJCTT-V36P126

MLA

Prerana Khurana, Shabnam Parveen "Effective Hybrid Recommender Approach using Improved K-means And Similarity". International Journal of Computer Trends and Technology (IJCTT) V36(3):147-152, June 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
In this age of information load, it become a herculean task for user to get the relevant information. Recommender system plays an important role in suggesting relevant information that is likely to be preferred by the user. Different type of clustering is used for recommender system like K-means, fuzzy C-mean, chameleon hierarchical etc. This papers aims at proposing a recommender system that uses hybrid approach using improved K-means clustering with Spearman’s rank correlation similarity to reduce the RMSE and time complexity and results are compared with basic K-means clustering.

References
[1] Harpreet Kaur Virk, Er.Maninder Singh,” Analysis and Design of Hybrid Online Movie Recommender System” International Journal of Innovations in Engineering and Technology (IJIET)Volume 5 Issue 2,April 2015.
[2] Sanjeev Dhawan, Kulvinder Singh, Jyoti,” High Rating Recent Preferences Based Recommendation System” 4thInternational Conference on Eco-friendly Computing and Communication Systems,ICECCS 2015
[3]Manoj Kumar,D.K Yadav,Ankur Singh,Vijay Kr. Gupta,” A Movie Recommender System: MOVREC” International Journal of Computer Applications (0975 – 8887)Volume 124 – No.3, August 2015
[4] Manisha Chandak,Sheetal Girase, Debajyoti Mukhopadhyay,” Introducing Hybrid Technique for Optimization of Book Recommender System” International Conference on Advanced Computing Technologies and Applications(ICACTA-2015)
[5] Utkarsh Gupta1 and Dr Nagamma Patil2,” Recommender System Based on Hierarchical Clustering Algorithm Chameleon” 2015 IEEE International Advance Computing Conference (IACC)
[6] Hirdesh Shivhare, Anshul Gupta, Shalki Sharma,” Recommender system using fuzzy c means clustering and genetic algorithm based weighted similarity measure” IEEE International Conference on Computer, Communication and Control (IC4-2015)
[7] Jyoti Gupta, Jayant Gadge,” Performance Analysis ofRecommendation System Based On Collaborative Filtering and Demographics”2015 International Conference on Communication, Information & Computing Technology (ICCICT), Jan. 16-17, Mumbai, India
[8] Zebin Wu,Yan Chen,Taoying Li,”Personalized Recommendation Based On The Improved Similarity and Fuzzy Clustering” The National Natural Science Foundation of China(No.71271034)2014 IEEE
[9] Hideyuki Mase, Hayato Ohwada,” A Collaborative Filtering Incorporating Hybrid-Clustering Technology” 2012 International Conference on Systems and Informatics (ICSAI 2012)
[10] Li Chao, Yu Jian, Li Xiang, Chen Jia Hui,” A Social Network System Oriented Hybrid Recommendation Model” 2012 2nd International Conference on Computer Science and Network Technology
[11] L. Martínez, R.M. Rodríguez, M. Espinilla,” REJA: A GEOREFERENCED HYBRID RECOMMENDER SYSTEM FOR RESTAURANTS” 2009 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology
[12]Joydeep Das , Shreya Dugar, Harsh Gupta, Subhashis Majumder and Prosenjit Gupta,” An Adaptive Approach To Collaborative Filtering Using Attribute Autocorrelation”2015 IEEE
[13]Ying Liu, Jiajun Yang,” Improving Ranking-based Recommendation by Social Information and Negative Similarity” Procedia Computer Science 55 ( 2015 ) 732 – 740
[14]Mohammed Wasid and Vibhor Kant,” A Particle Swarm Approach to Collaborative Filtering based Recommender Systems through Fuzzy Features” Eleventh International Multi- Conference on Information Processing-2015 (IMCIP-2015), Procedia Computer Science 54 ( 2015 ) 440 – 448
[15] Oleksandr Krasnoshchok, Yngve Lamo,” Extended contentboosted matrix factorization algorithm for recommender systems” 18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems - KES2014, Procedia Computer Science 35 ( 2014 ) 417 – 426
[16] Prerana Khurana, Shabnam Parveen, “Approaches of Recommender System: A Survey”, International of Computer Trends and Technology (IJCTT) – Volume 34 Number 3 - April 2016
[17]]Margaret H. Dunham, Data Mining- Introductory and Advanced Concepts, Pearson Education, 2006
[18] K. A. Abdul Nazeer, M. P. Sebastian,” Improving accuracy of recommendation system by means of Item-based Fuzzy Clustering Collaborative Filtering”2011 IEEE
[19] Rajani Chulyadyo, Philippe Leray ,” A personalized recommender system from probabilistic relational model and users’ preferences” 18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems - KES2014, Procedia Computer Science 35 ( 2014 ) 1063 – 1072
[20] Dr. Sarika Jain, Anjali Grover, Praveen Singh Thakur, Sourabh Kumar Choudhary,” Trends, Problems And Solutions of Recommender System” International Conference on Computing, Communication and Automation (ICCCA2015)
[21] Saikat Bagchi,” Performance and Quality Assessment of Similarity Measures in Collaborative Filtering Using Mahout” 2nd International Symposium on Big Data and Cloud Computing (ISBCC’15), Procedia Computer Science 50 ( 2015 ) 229 – 234

Keywords
Recommender System, Hybrid Recommender, clustering, k-means, similarity, RMSE, Spearman's rank correlation.