Centroid Selection Approaches for K-Means Clustering based Recommender Systems

SEEE DIGIBOOK ON ENGINEERING & TECHNOLOGY, VOL. 01, FEB 2018 PP.(274-281)
Abstract– Recommender system suggests products to users based on the preference of similar users. It is used in e-commerce system and helps to match users with items. If any active user searches an item, the system recommends products based on the items searched by other users who have similar interest, hence it avoids information overloading problem. Over the past few years, recommender system suffers from scalability and sparsity issues. Different traditional methods had been employed but it does not solve such issues effectively. This paper addresses these issues by using K-Means clustering algorithms. Different centroid selection algorithms had been studied to determine the performance of K-means algorithm. The proposed algorithm uses model based collaborative filtering with offline clustering method. This makes searching easier and reduces the latency of search. Hence, the drawbacks of memory based collaborative filtering of recommender systems had been overcomed. In this paper, the implementation of model based collaborative filtering uses various types of K-Means or Clustering algorithms.
Index Terms – Data Mining, Clustering, Content-Based Filtering, Collaborative-Based Filtering, Recommender System, Memory-Based Recommender System, Model-Based Recommender System.
REFERENCE

[1] Sobia Zahra, Mustansar Ali Ghazanfar, Asra Khalid, Muhammad Awais Azam, Usman Naeem, Adam Prugel Bennett, “ Novel centroid selection approaches for KMeans-clustering based recommender systems ”, Information Sciences Vol. 320, pp. 156-189, May 2015.
[2] Nguyen Thai-Nghe, Lucas Drumond, Artus Krohn-Grimberghe, Lars Schmidt-Thieme, “ Recommender System for Predicting Student Performance ”, Procedia Computer Science Vol.1, pp. 2811-2819, 2010.
[3] Woon-hae Jeong, Se-jun Kim, Doo-soon Park and Jin Kwak, “ Performance Improvement of a Movie Recommendation System based on PersonalPropensity and Secure Collaborative Filtering ”, J Inf Process Syst, Vol.9, No.1, March 2013.
[4] Gediminas Adomavicius , YoungOk Kwon, “ New Recommendation Techniques for Multi-Criteria Rating Systems ”, Gediminas Adomavicius and Young Ok Kwon, University of Minnesota, IEEE Intelligent System, Vol. 22, Issue. 3, May-June 2007.
[5] Qi Wang, Wei Cao and Yun Liu,“A Novel Clustering Based Collaborative Filtering Recommendation System Algorithm ”, Springer Science, Vol.260, pp 673-680, November 2013.
[6] Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl, “ ItemBased Collaborative Filtering Recommendation Algorithms ”, ACM Proceedings of the 10th international conference on World Wide Web, No.01, pp. 285-295, May 01-05, 2001.
[7] Gui-Rong Xue1, Chenxi Lin1, Qiang Yang3, WenSi Xi4, Hua-Jun Zeng2, Yong Yu1, Zheng Chen2, “Scalable Collaborative Filtering Using Cluster-based Smoothing* ”, ACM, August 15–19, 2005.
[8] Nagaraj, B., P. Muthusami, and N. Murugananth. “Optimum PID Controller Tuning Using Soft computing Methodologies for Industrial Process.” Karpagam Journal of Computer Science: 1761.
[9] Vidhya, S., and B. Nagaraj. “Fuzzy based PI Controller for Basis Weight Process in Paper Industry.” Fuzzy Systems 4.7 (2012): 268-272.
[10] Nagaraj, B., and P. Vijayakumar. “CONTROLLER TUNING FOR INDUSTRIAL PROCESS-A SOFT COMPUTING APPROACH.” Int. J. Advance. Soft Comput. Appl 4.2 (2012).
[11] Nagaraj, B., and P. Vijayakumar. “Bio Inspired Algorithm for PID Controller Tuning and Application to the Pulp and Paper Industry.” Sensors & Transducers 145.10 (2012): 149.
[12] Lu Yang, Anilkumar Kothalil Gopalakrishnan, “A Collaborative Filtering Recommendation Based on User Profile and User Behavior in Online Social Networks”, IEEE International Computer Science and Engineering Conference 14, 2014.
[13] Nitin Pradeep Kumar, Zhenzhen Fan, “ Hybrid User-Item Based Collaborative Filtering ”, Procedia Computer Science 60, pp. 1453 – 1461, 2015.
[14] Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Miguel A. Rueda-Morales “ Combining content-based and collaborative recommendations A hybrid approach based on Bayesian networks ”, International Journal of Approximate Reasoning 51, pp. 785–799, April 2010.
[15] Gang Chen, Fei Wang, Changshui Zhang, “ Collaborative filtering using orthogonal nonnegative matrix tri-factorization ”, Information Processing and Management 45, pp. 368–379, January 2009.
[16] Tae Hyup Roha, Kyong Joo Ohb, Ingoo Hana, “ The collaborative filtering recommendation based on SOM cluster-indexing CBR ”, Expert Systems with Applications 25, pp. 413–423, 2003.
[17] Kai Yu, Anton Schwaighofer, Volker Tresp, Xiaowei Xu, and Hans-Peter Kriegel, “ Probabilistic Memory-Based Collaborative Filtering ”, IEEE Transactions On Knowledge and Data Engineering, Vol. 16, No. 1, January 2004

P. Masethungh, S. Kumaresan,
Government College of Technology,
Coimbatore, India.
masethungh@gmail.com,
sukumaresan@gct.ac.in

Leave a Comment

Your email address will not be published. Required fields are marked *

Scroll to Top