Authors:
Neetu Singh,V.K Jain ,DOI NO:
https://doi.org/10.26782/jmcms.2018.10.00016Keywords:
Recommender System, Cellular networks,Similarities,data plans,Similarity fusion,Abstract
Recommendations help humans in making decisions and hence contribute in increase of user satisfaction. For good recommendations; the recommender should be more precise. From past decades, Collaborative Filtering (CF) has been explored by researchers because of its efficiency and effectiveness. The main objective of CF is to find most similar items using various similarity measures. This research paper proposes improvised mobile recommender that significantly increases accuracy for recommended right plans for mobile users using similarity fusion. Experimental results show that the proposed recommender using similarity fusions provide better recommendation quality.Refference:
I.Anand, S. S., & Mobasher, B. Intelligent techniques for web personalization. In Proceedings of the 2003 International Conference on Intelligent Techniques for Web Personalization (pp. 1–36). Springer-Verlag(2003).
II.Blondel VD, Decuyper A, Krings G,” A survey of results on mobile phone datasets analysis”. EPJ Data Sci , pp 4-10(2015).
III.C. Porcel, E. Herrera-Viedma,“Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries”, Knowledge-Based Systems, vol. 23, No. 1, pp 32–39 (2010).
IV.C. Porcel, J.M. Moreno, E. Herrera-Viedma,” A multi-disciplinar recommender system to advice research resources in university digital libraries”, Expert Systems with Applications, vol. 36 , No. 10, pp 12520–12528 (2009).
V.Deshpande, M., Karypis, G. “Item-Based Top-N Recommendation Algorithms”. ACM Trans. On Information Systems(2004).
VI.Hill, W., Stead, L., Rosenstein, M., and Furnas, G., “Recommending and evaluating choices in a virtual community of use”, Proceedings of the ACM(CHI’95), New York, 1995, pp. 194-201,(1995).
VII.Kantor, P. B., Ricci, F., Rokach, L., & Shapira, B. (2011). Recommender systems handbook. Springer.
VIII.Mahmood, T., & Ricci, F. Improving recommender systems with adaptive conversational strategies. In Proceedings of the 20th ACM Conference on Hypertext and Hypermedia (pp. 73–82) (2009).
IX.Marlin, B. “Collaborative Filtering: A Machine Learning Perspective”. Phd thesis. University of Toronto ( 2004).
X.Meenakshi, Prof. Pravin Nimbalkar,” An Improvised Recommendation System on Top-N, Unrated and Point of Interest Recommendations Regularized with User Trust and Item Ratings”,IJRCCE,Vol. 5, Issue 8,( 2017).
XI.Miritello G, Rubén L, Cebrian M, Moro E,” Limited communication capacity unveils strategies for human interaction”. Sci Rep 3:1950 (2013).
XII.M.Kavitha devi, P.Venkatesh, ̳An ImprovedCollaborative Recommender System„2009 First International Conference on Networks &Communications-© 2009 IEEE DOI 10.1109/NetCoM.2009.69(2009).
XIII.Neetu Singh, Puneet Kumar & Anil Kumar Dahiya,” RWYW: Recommend What You Want -A Recommender for Mobile Plans”, International Journal of Innovations & Advancement in Computer Science,Vol. 7, No.2 , pp 135-144 ( 2018).
XIV.Neetu Singh, V.K Jain,”A novel Item Recommender for mobile plans”.IJCSIS , Vol. 16 No. 9,(2018).
XV.Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P.,and Riedl, J., “GroupLens: an open architecture for collaborative filtering of Netnews”, Proceedings of the CSCW conference, Chapel Hill, NC, pp.175-186.( 1994).
XVI.Rongfei, J., Maozhong, J., & Chao, L. A new clustering method for collaborative filtering. In International Conference on Networking and Information Technology (pp. 488–492) (2010).
XVII.Rucker, J., and Polanco, M.J., “Personalized navigation for the Web”, Communications of the ACM, March, 40(3), pp. 73-75,(1997).
XVIII.Saaty, T.L., “Fundamentals of decision making andpriority theory with the analytic hierarchy process”,RWS Publications, Pittsburgh, PA, (1994).
XIX.Sarwar, B. M., Karypis, G., Konstan, J., Riedl, J. 2001. “Item-Based Collaborative Filtering Recommendation Algorithms”.WWW (2001).
XX.The mahout website.” http://mahout.apache.org/
Author(s): Neetu Singh, V.K Jain View Download