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Cascaded Feature Selection for Enhancing the Performance of Collaborative Recommender SystemAL-SHAMRI, M. Y. H. , AL-JUNIAD, A. F. , QAID, T. S. , AHMED, M. H. A. , RAWEH, A. A.
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computational modeling, feature extraction, information filtering, machine learning, recommender systems
systems(18), recommender(16), learning(9), machine(8), data(8), collaborative(7), system(6), selection(6), recommendation(6), filtering(6)
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About this article
Date of Publication: 2018-11-30
Volume 18, Issue 4, Year 2018, On page(s): 23 - 34
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.04003
Web of Science Accession Number: 000451843400003
SCOPUS ID: 85058813839
Most of collaborative recommender systems (CRSs) rely on statistical and data analysis methods for comparing users. However, dealing with them using machine learning techniques seems to be more appropriate. This paper investigates the usage of feature selection and classification methods for CRSs. It suggests building a user model suitable for the classification purpose and proposes a density-based feature selection (DBFS) method based on the rating density for each class. The DBFS reduces the effect of sparsity problem and keeps only users having a dense-feature history. Additionally, a cascaded feature selection method is proposed to pick out a subset of features through a two-layer approach. The first layer applies a classical feature selection method while the second layer applied the DBFS on the output of the first layer. The results show that the performance is gradually improved. The cascaded feature selection yields the best results since it improves the system accuracy, reduces the space and processing complexities, and alleviates the sparsity in two cascaded layers. The achieved improvements by cascaded feature selection as compared to SVM are 6.55 percent, 10.14 percent, and 3.92 percent in terms of accuracy, F-measure and MAE, respectively.
|References|||||Cited By «-- Click to see who has cited this paper|
| D. Donoho, "High-dimensional data analysis: the curses and blessings of dimensionality," in Proc. American Mathematical Society Conference of Math Challenges of the 21st Century, 2000.
 I. Portugal, P. Alencar, and D. Cowan, "The use of machine learning algorithms in recommender systems: a systematic review," arXiv, vol. 4, pp. 1-16, Nov. 2015.
 J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, "Recommender systems survey," Knowledge-Based Systems, vol. 46, pp.109-132, 2013.
[CrossRef] [Web of Science Times Cited 863]
 M. D. Ekstrand, J. T. Riedl, and J. A. Konstan, "Collaborative filtering recommender systems," Foundations and Trends in Human-Computer Interaction, vol. 4, No. 2, pp. 81-173 2010.
 F. Isinkaye, Y. Folajimi, and B.Ojokoh, "Recommendation systems: Principles, methods and evaluation," Egyptian Informatics Journal, vol. 16, pp. 261-273, 2015.
[CrossRef] [Web of Science Times Cited 80]
 X. Su and T. M. Khoshgoftaar, "A survey of collaborative filtering techniques," Advances in Artificial Intelligence, vol. 2009, pp. 1-9, 2009.
 M. Y. H. Al-Shamri, "User profiling approaches for demographic recommender systems," Knowledge-Based Systems, vol. 100, pp. 175-187, 2016.
[CrossRef] [Web of Science Times Cited 34]
 R. Burke, "Hybrid recommender systems: Survey and experiments," User Modeling and User-Adapted Interaction, vol. 12, No. 4, pp. 331-370, 2002.
[CrossRef] [Web of Science Times Cited 1354]
 J. S. Breese, D. Heckerman, and C. Kadie, "Empirical analysis of predictive algorithms for collaborative filtering," in Proc. 14th conference on Uncertainty in artificial intelligence, 1998, pp. 43-52.
 I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh, Feature Extraction: Foundations and Applications. Springer, pp. 463-470, 2006.
 L. Yu and H. Liu, "Efficient feature selection via analysis of relevance and redundancy", Journal of Machine Learning Research, vol. 5, pp. 1205-1224, 2004.
 V. B. Canedo, "Novel feature selection methods for high dimensional data," PhD thesis, March 2014.
 C. Basu, H. Harish, W. Cohen, "Recommendation as classification: Using social and content-based information in recommendation," AAAI Technical Report, WS-98-08, 1998.
 L. Schmidt-Thieme, "Compound classification models for recommender systems," in Proc. 5th IEEE International Conference on Data Mining, 2005, pp. 378-385.
[CrossRef] [Web of Science Times Cited 3]
 K. Wang, Y. Tan, "A new collaborative filtering recommendation approach based on naive Bayesian method," in: Tan Y., Shi Y., Chai Y., Wang G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, vol. 6729, pp. 218-227, 2011.
 K. Miyahara and M. J. Pazzani, "Improvement of collaborative filtering with the simple Bayesian classifier," IPSJ journal, vol. 43, no. 11, pp. 3429-3437, 2002.
 D. Bouneffouf, A. Bouzeghoub, and A. L. Gançarski, "Hybrid- ?-greedy for mobile context-aware recommender system," in Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer Berlin Heidelberg, 2012, pp. 468-479.
 A. I. Saleh, A. I. El Desouky, and S. H. Ali, "Promoting the performance of vertical recommendation systems by applying new classification techniques," Knowledge-Based Systems, vol. 75, pp. 192-223, 2015.
[CrossRef] [Web of Science Times Cited 11]
 B. Wang, Q. Liao, and C. Zhang, "Weight based KNN recommender system," in Proc. 5th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 26-27 August 2013, pp. 449-452.
 A. Bouza, G. Reif, A. Bernstein, and H. Gall, "SemTree: ontology-based decision tree algorithm for recommender systems," in Proc. Semantic Web Conference, Karlsruhe, Germany, 2008.
 Y. Cho, J. Kim, and S. Kim, "A personalized recommender system based on web usage mining and decision tree induction," Expert Systems with Application, vol. 23, No, 2, pp. 329-342, 2002.
[CrossRef] [Web of Science Times Cited 198]
 D. Nikovski and V. Kulev, "Induction of compact decision trees for personalized recommendation," in Proc. ACM Symposium on Applied Computing, Dijon, France, April 2006, pp. 575-581.
 W. Cheng, J. Hühn, and E. Hüllermeier. "Decision tree and instance-based learning for label ranking," in Proc. 26th Annual International Conference on Machine Learning (ICML 09), New York, NY, USA, 2009, pp. 161-168.
 X. Su and T. M. Khoshgoftaar, "Collaborative filtering for multi-class data using belief nets algorithms," International Journal on Artificial Intelligence Tools, vol. 17, No. 1, pp. 71-85, 2008.
[CrossRef] [Web of Science Times Cited 7]
 T. Zhang and V. S. Iyengar, "Recommender systems using linear classifiers," Journal of Machine Learning Research, vol. 2, pp. 313-334, 2002.
 A Gershman, A. Meisels, K-H. Luke, L. Rokach, A. Schclar, and A. Sturm, "A decision tree based recommender system," in Proc. 10th Conf. on Innovative Internet Community Services, Trondheim, Norway, 2010, pp. 170-179.
 T. Zhang and F. Ma, "Improved rough k-means clustering algorithm based on weighted distance measure with Gaussian function", International Journal of Computer Mathematics, vol. 94, no. 4, pp. 663-675, 2017.
[CrossRef] [Web of Science Times Cited 8]
 I. D. Borlea, R. E. Precup, F. Dragan and A. B. Borlea, "Centroid update approach to k-means clustering", Advances in Electrical and Computer Engineering, vol. 17, no. 4, pp. 3-10, 2017.
[CrossRef] [Full Text] [Web of Science Times Cited 5]
 S. Chakraborty and S. Das, "k-means clustering with a new divergence-based distance metric: Convergence and performance analysis", Pattern Recognition Letters, vol. 100, pp. 67-73, 2017.
[CrossRef] [Web of Science Times Cited 8]
 S. Zahra, M. A. Ghazanfar, A. Khalid, M. A. Azam, U. Naeem, A. P. Bennett, "Novel Centroid Selection Approaches for K-Means Clustering Based Recommender Systems", Information Sciences, Vol. 320, pp. 156-189, Nov. 2015.
[CrossRef] [Web of Science Times Cited 44]
 S. Sharma, "A Recommender System Based on Improvised K- Means Clustering Algorithm", International Journal of Research in Advent Technology, Vol. 6, No. 7, pp. 1477-1483, July 2018.
 J. Bobadilla, R. Bojorque, A. H. Esteban, and R. Hurtado, "Recommender Systems Clustering Using Bayesian Non Negative Matrix Factorization", IEEE Access, Vol. 6, pp. 3549-3564, 2018.
 C. C. Aggarwal, Data Mining: The Textbook, Springer, pp. 285-343, 2015.
 S. Yu, Z-H Zhou, M. Steinbach, D. J. Hand, and D. Steinberg, "Top 10 algorithms in data mining," Knowledge Information Systems, vol. 14, pp. 1-37, 2008.
[CrossRef] [Web of Science Times Cited 1689]
 T. K. Ho, "Random decision forests," in Proc.3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14-16 August 1995, pp. 278-282.
 C. Cortes, and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, pp. 273-297, 1995.
 I. Guyon, and A. Elisseeff, "An introduction to variable and feature selection," Journal of Machine Learning Research, vol. 3, pp. 1157-1182, 2003.
 V. Bolón-Canedo, N. Sanchez-Marono, and A. Alonso-Betanzos, Feature Selection for High-Dimensional Data, Springer, pp. 31-40, 2015.
 G. Chandrashekar, and F. Sahin, "A survey on feature selection methods," Computers and Electrical Engineering, vol. 40, pp. 16-28, 2014.
[CrossRef] [Web of Science Times Cited 703]
 M. Y. H. Al-Shamri, Effect of Collaborative Recommender System Parameters: Common Set Cardinality and the Similarity Measure, Advances in Artificial Intelligence 2016 (2016), 10 pages
 A. Al-Junaid, T. Qaid, MYH Al-Shamri, M. Ahmed, A Raweh, Vertical and Horizontal DNA Differential Methylation Analysis for Predicting Breast Cancer, IEEE Access, vol. 6, pp. 53533-53545, 2018.
[CrossRef] [Web of Science Times Cited 1]
 N. Japkowicz and M. Shah, Evaluating learning algorithms: a classification perspective, Cambridge, pp. 74-159, 2011.
 A. Zhang, Evaluating Machine Learning Models: A Beginner Guide to Key Concepts and Pitfalls, O'Reilly Media, pp. 7-12, 2015.
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