INFORMATION CHANGE THE WORLD

International Journal of Information Engineering and Electronic Business(IJIEEB)

ISSN: 2074-9023 (Print), ISSN: 2074-9031 (Online)

Published By: MECS Press

IJIEEB Vol.11, No.6, Nov. 2019

Feature Engineering based Approach for Prediction of Movie Ratings

Full Text (PDF, 743KB), PP.24-31


Views:101   Downloads:10

Author(s)

Sathiya Devi S, Parthasarathy G

Index Terms

Recommender systems;gradient boost regression;supervised learning; feature engineering

Abstract

The buying behavior of the consumer is grown nowadays through recommender systems. Though it recommends, still there are limitations to give a recommendation to the users. In order to address data sparsity and scalability, a hybrid approach is developed for the effective recommendation in this paper.  It combines the feature engineering attributes and collaborative filtering for prediction. The proposed system implemented using supervised learning algorithms. The results empirically proved that the mean absolute error of prediction was reduced. This approach shows very promising results.

Cite This Paper

Sathiya Devi S, Parthasarathy G, "Feature Engineering based Approach for Prediction of Movie Ratings", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.11, No.6, pp. 24-31, 2019. DOI: 10.5815/ijieeb.2019.06.04

Reference

[1]J. Bobadilla, F. Ortega, A. Hernando, and A. Gutierre, “Recommender systems survey” ,Journal of Knowledge Based Systems, 2013, 103-132.

[2]Francesco Ricci, Lior Rokach ,Bracha Shapira  and Paul B. Kantor, “Recommender Systems Handbook”, Springer, e-ISBN 978-0-387-85820-3,2011, 1-35. 

[3]C.C Aggarwal,” Recommender Systems: The Textbook”, Springer International Publishing Switzerland 2016.

[4]Robin Burke,“Hybrid Recommender Systems: Survey and Experiments”,User Modelinga nd User-Adapted Interaction 12: 331^370, 2002..Burke, R. User Model User-Adap Inter (2002) 12: 331. https://doi.org/10.1023/A:1021240730564

[5]Xiaoyuan Su and Taghi M. Khoshgoftaar “A Survey of Collaborative Filtering Techniques”. Hindawi Publishing Corporation,Advances in Artificial  Intelligence Volume 2009, Article ID 421425, 19 pages

[6]Nour El Islem Karabadji, Samia Beldjoudi, Hassina Seridi, Sabeur Aridhi, Wajdi Dhifli, Improving Memory-Based User Collaborative Filtering with Evolutionary Multi-Objective Optimization, Expert Systems With Applications (2018), doi: 10.1016/j.eswa.2018.01.015

[7]Maryam Khanian Najafabadi, Azlinah Mohamed, Choo Wou Onn, “An impact of time and item influencer in collaborative filtering recommendations using graph-based model”, Journal of Information and Processing and Management, 2019,pp 526-540

[8]J. Deng, J. Guo and Y. Wang, A Novel K-medoids clustering recommendation algorithm based on probability distribution for collaborative filtering, Knowledge-Based Systems (2019), https://doi.org/10.1016/j.knosys.2019.03.009

[9]Abdullah Almuhaimeed  and Maria Fasli, “A Hybrid Semantic Method for Enhancing Movie Recommendations”, IEEE Transactions on ,2017

[10]Tulasi K. Paradarami, Nathaniel D. Bastian, Jennifer L. Wightman, A Hybrid Recommender System Using ArtiÞcial Neural Networks, Expert Systems With Applications (2017), doi:10.1016/j.eswa.2017.04.046

[11]Avadhut D.Wagavkar and S.S.Vairagar, “Weighted Hybrid Approach in Recommendation Method”, International Journal of Computer Science Trends and Technology (IJCST) – Volume 5 Issue 2, Mar – Apr 2017, 5 pages

[12]Mustansar Ali Ghazanfar and Adam Pr¨ugel-Bennett, “Building Switching Hybrid Recommender System Using Machine Learning Classifiers and Collaborative Filtering”, IAENG International Journal of Computer Science, 37:3, IJCS_37_3_09

[13]Chen, W., Niu, Z., Zhao, X. et al., World Wide Web (2014) 17: 271. https://doi.org/10.1007/s11280-012-0187-z

[14]Manisha Chandaka, Sheetal Giraseb, Debajyoti Mukhopadhyayc, “Introducing Hybrid Technique for Optimization of Book Recommender System”, Procedia Computer Science 45 ( 2015 ) 23 – 31

[15]Felipe F. Bocca, Luiz Henrique Antunes Rodrigues, “The effect of tuning, feature engineering, and feature selection in data mining applied to rainfed sugarcane yield modelling”, Journal of Computers and Electronics in Agriculture 128 (2016) 67–76.

[16]Chuan Zhang, Liwei Cao, Alessandro Romagnoli, On the feature engineering of building energy data mining, <![CDATA[Sustainable Cities and Society]]>(2018), https://doi.org/10.1016/j.scs.2018.02.016

[17]Tara Rawat and Vineeta Khemchandani, “Feature Engineering (FE) Tools and Techniques for Better Classification Performance”, International Journal of Innovations in Engineering and Technology (IJIET),http://dx.doi.org/10.21172/ijiet.82.024,2017.

[18]Jeff Heaton, “An Empirical Analysis of Feature Engineering for Predictive Modeling’arXiv:1701.07852v1 [cs.LG] 26 Jan 2017.

[19]Nikita D. Patel , Chetana Chand, “Selecting Best Features Using Combined Approach in POS Tagging for Sentiment Analysis”, IJCSMC, Vol. 3, Issue. 3, March 2014, 425 – 430

[20]Hsiang-Fu Yu, “Feature Engineering and Classifier Ensemble for KDD Cup 2010”,JMLR: Workshop and Conference Proceedings 1: 1-16

[21]Alejandro Correa Bahnsen, Djamila Aouada, Aleksandar Stojanovic,Björn Ottersten,“Feature engineering strategies forcre ditcard fraud detection”,Expert Systems With Applications 51(2016)134–142

[22]JiaweiHan Micheline Kamber ,JianPei, “Data Preprocessing-Data Mining (Third Edition) The Morgan Kaufmann Series in Data Management Systems 2012,  83-124

[23]Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, (2011), 2825-2830.

[24]Senthil Kumar P., Daphne Lopez, “A Review on Feature Selection Methods for High Dimensional Data”, International Journal of Engineering and Technology,e-ISSN : 0975-4024,1-4

[25]John McCall, “Genetic algorithms for modelling and optimisation”, Journal of Computational and Applied Mathematics 184 (2005) 205–222

[26]Chuan Zhang, Liwei Cao, Alessandro Romagnoli, On the  feature engineering of building energy data mining, Journal of Sustainable Cities and Society,(2018), https://doi.org/10.1016/j.scs.2018.02.016

[27]Abinash Tripathy, Ankit Agrawal, Santanu Kumar Rath, “Classification of Sentimental Reviews Using Machine Learning Techniques’, Procedia Computer Science 57 ( 2015 ) 821 – 829

[28]Gulden Kaya Uyanik,Nese Guler,“A study on Multiple linear regression analysis”,Procedia - Social and Behavioral Sciences 106 ( 2013 ) 234 – 240

[29]Tong Xiao , Jingbo Zhu , Tongran Liu, “Bagging and Boosting statistical machine translation systems”,Journal of Artificial Intelligence 195 (2013) 496–527

[30]J. Bobadilla , F. Ortega, A. Hernando, A. Gutiérrez, “Recommender systems survey”,Knowledge-Based Systems 46 (2013) 109–132

[31]https://grouplens.org/datasets/movielens/100k/last accessed on 12.12.2017

[32]Yousef Kilani ,Ahmed Fawzi Otoom ,Ayoub Alsarhan  and Manal Almaayah, “Genetic Algorithms-Based Hybrid Recommender System of Matrix Factorization and Neighborhood-Based”,Journal of Computational Science (2018), https://doi.org/10.1016/j.jocs.2018.08.007

[33]Marlis Ontivero-Ortega a, Agustin Lage-Castellanos a,c, Giancarlo Valente c, Rainer Goebel c, Mitchell Valdes-Sosa, “Fast Gaussian Naïve Bayes for searchlight classification analysis”,Journal of NeuroImage,2017, 1-9

[34]Feng J, Feng X, Zhang N, Peng J (2018) An improved collaborative filtering method based on similarity. PLoS ONE13(9):e0204003. https://doi.org/10.1371/journal.pone.0204003