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AN ENSEMBLE MODEL FOR COLLABORATIVE FILTERING TO INVOLVE ALL ASPECTS OF DATASET

Year 2018, Volume: 60 Issue: 2, 15 - 26, 01.08.2018

Abstract

The accuracy of
predictions is better if the combinations of the different approaches are used.
Currently in collaborative filtering research, the linear blending of various
methods is used. More accurate classifiers can be obtained by combining less
accurate ones. This approach is called ensembles of classifiers. Different
collaborative filtering methods uncover the different aspects of the dataset.
Some of them are good at finding out local relationships; the others work for
the global characterization of the data. Ensembles of different collaborative
filtering algorithms can be created to provide more accurate recommender
systems.

References

  • Herlocker J. L., Konstan J.A., Terveen L.G., Riedl J. T., Evaluating Collaborative Filtering Recommender Systems, ACM Transactions on Information Systems, Vol. 22. No. 1. January 2004.
  • Pryor M. H., The effects of Singular Value Decomposition on Collaborative Filtering, Computer science Technical Report, Dartmouth College, PCS-TR98-338, June 1998
  • Linden G., Smith B., York J., Amazon.com Recommendations Item-to-Item Collaborative Filtering, IEEE Internet Computing, January-February 2003.
  • Bell R. M., Koren Y., Improved Neighborhood-Based Collaborative Filtering, KDDCup’07 August 2007.
  • Bell R. M., Koren Y., Volinsky C., Modeling Relationships at Multiple Scales to Improve Accuracy of Large Recommender Systems, KDDCup’07 August 2007.
  • Töscher A., Jahrer M., Legenstein R., Improved Neighborhood-Based Algorithms for Large-Scale Recommender Systems, 2nd Netflix-KDD Workshop, August 2008.
  • Wu M., Collaborative Filtering via Ensembles of Matrix Factorizations, KDDCup 07, August 2007.
  • Bell R. M., Koren Y., Volinsky C., Solution to the Netflix Prize, The BellKor 2008
  • Su X., Khoshgoftaar T. M., Greiner R., Imputation-Boosted Collaborative Filtering Using Machine Learning Classifiers, SAC 08, March 2008.
  • Koren Y., Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering, KDD 08, August 2008.
  • Kuncheva L. I., Skurichina M., Duin R.P.W., An Experimental Study on Diversity for Bagging and Boosting with Linear Classifiers, Information Fusion 3 (2002).
  • Yang J., Li K. F., Zhang D., Recommendation Based on Rational Inferences in collaborative filtering, Knowledge-Based Systems 22 (2009).
  • Sharkey A. J. C., Types of Multinet system, MCS 2002, LNCS 2364, 2002.
  • Brown G., Wyatt j., Harris R., Yao X., Diversity Creation Methods: A Survey and Categorization, Information Fusion xxx (2004).
  • Waterhouse S., MacKay D., Robinson T., Bayesian Methods for Mixtures of Experts, Neural Information Processing Systems 8.
  • Jacobs R. A., Jordan M. I., Barto A. G., The Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks, COINS Technical Report 90-27, March 1990.
  • Dietterich T. G., Ensemble Methods in Machine Learning, MCS 2000, LNCS, 2000.
  • Perrone M. P., Cooper L. N., When Networks Disagree: Ensemble Methods for Hybrid Neural Networks, Neural Networks for Speech and Image Processing, 1993.
  • Opitz D., Maclin R., Popular Ensemble Methods: an Empirical Study, Journal of Artificial Intelligence Research 11 (1999).
  • Shi H., Lv Y., An Ensemble Classifier Based on Attribute Selection and diversity Measure, Fifth International Conference on fuzzy Systems and Knowledge Discovery 2008.
  • Brown G., Yao X., On the Effectiveness of Negative Correlation Learning, First UK Workshop and Computational Intelligence UKCI 01, September 2001.
  • Zanda M., Brown G., Fumera G., Roli F., Ensemble Learning in Linearly Combined Classifiers via Negative Correlation, MCS 2007, LNCS 4472, 2007.
  • Liu Y., Yao X., Ensemble Learning via Negative Correlation, Neural Networks 12 (1999).
  • Liu Y., Yao X., Higuchi T., Evolutionary Ensembles with Negative Correlation Learning, IEEE Transactions on Evolutionary Computation, November 2000.
  • Ar Y., Bostanci E., A genetic algorithm solution to the collaborative filtering problem, Expert Systems with Applications, November 2016.
Year 2018, Volume: 60 Issue: 2, 15 - 26, 01.08.2018

Abstract

References

  • Herlocker J. L., Konstan J.A., Terveen L.G., Riedl J. T., Evaluating Collaborative Filtering Recommender Systems, ACM Transactions on Information Systems, Vol. 22. No. 1. January 2004.
  • Pryor M. H., The effects of Singular Value Decomposition on Collaborative Filtering, Computer science Technical Report, Dartmouth College, PCS-TR98-338, June 1998
  • Linden G., Smith B., York J., Amazon.com Recommendations Item-to-Item Collaborative Filtering, IEEE Internet Computing, January-February 2003.
  • Bell R. M., Koren Y., Improved Neighborhood-Based Collaborative Filtering, KDDCup’07 August 2007.
  • Bell R. M., Koren Y., Volinsky C., Modeling Relationships at Multiple Scales to Improve Accuracy of Large Recommender Systems, KDDCup’07 August 2007.
  • Töscher A., Jahrer M., Legenstein R., Improved Neighborhood-Based Algorithms for Large-Scale Recommender Systems, 2nd Netflix-KDD Workshop, August 2008.
  • Wu M., Collaborative Filtering via Ensembles of Matrix Factorizations, KDDCup 07, August 2007.
  • Bell R. M., Koren Y., Volinsky C., Solution to the Netflix Prize, The BellKor 2008
  • Su X., Khoshgoftaar T. M., Greiner R., Imputation-Boosted Collaborative Filtering Using Machine Learning Classifiers, SAC 08, March 2008.
  • Koren Y., Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering, KDD 08, August 2008.
  • Kuncheva L. I., Skurichina M., Duin R.P.W., An Experimental Study on Diversity for Bagging and Boosting with Linear Classifiers, Information Fusion 3 (2002).
  • Yang J., Li K. F., Zhang D., Recommendation Based on Rational Inferences in collaborative filtering, Knowledge-Based Systems 22 (2009).
  • Sharkey A. J. C., Types of Multinet system, MCS 2002, LNCS 2364, 2002.
  • Brown G., Wyatt j., Harris R., Yao X., Diversity Creation Methods: A Survey and Categorization, Information Fusion xxx (2004).
  • Waterhouse S., MacKay D., Robinson T., Bayesian Methods for Mixtures of Experts, Neural Information Processing Systems 8.
  • Jacobs R. A., Jordan M. I., Barto A. G., The Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks, COINS Technical Report 90-27, March 1990.
  • Dietterich T. G., Ensemble Methods in Machine Learning, MCS 2000, LNCS, 2000.
  • Perrone M. P., Cooper L. N., When Networks Disagree: Ensemble Methods for Hybrid Neural Networks, Neural Networks for Speech and Image Processing, 1993.
  • Opitz D., Maclin R., Popular Ensemble Methods: an Empirical Study, Journal of Artificial Intelligence Research 11 (1999).
  • Shi H., Lv Y., An Ensemble Classifier Based on Attribute Selection and diversity Measure, Fifth International Conference on fuzzy Systems and Knowledge Discovery 2008.
  • Brown G., Yao X., On the Effectiveness of Negative Correlation Learning, First UK Workshop and Computational Intelligence UKCI 01, September 2001.
  • Zanda M., Brown G., Fumera G., Roli F., Ensemble Learning in Linearly Combined Classifiers via Negative Correlation, MCS 2007, LNCS 4472, 2007.
  • Liu Y., Yao X., Ensemble Learning via Negative Correlation, Neural Networks 12 (1999).
  • Liu Y., Yao X., Higuchi T., Evolutionary Ensembles with Negative Correlation Learning, IEEE Transactions on Evolutionary Computation, November 2000.
  • Ar Y., Bostanci E., A genetic algorithm solution to the collaborative filtering problem, Expert Systems with Applications, November 2016.
There are 25 citations in total.

Details

Primary Language English
Journal Section Review Articles
Authors

YILMAZ Ar

Publication Date August 1, 2018
Submission Date October 17, 2018
Acceptance Date October 30, 2018
Published in Issue Year 2018 Volume: 60 Issue: 2

Cite

APA Ar, Y. (2018). AN ENSEMBLE MODEL FOR COLLABORATIVE FILTERING TO INVOLVE ALL ASPECTS OF DATASET. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 60(2), 15-26.
AMA Ar Y. AN ENSEMBLE MODEL FOR COLLABORATIVE FILTERING TO INVOLVE ALL ASPECTS OF DATASET. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. August 2018;60(2):15-26.
Chicago Ar, YILMAZ. “AN ENSEMBLE MODEL FOR COLLABORATIVE FILTERING TO INVOLVE ALL ASPECTS OF DATASET”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 60, no. 2 (August 2018): 15-26.
EndNote Ar Y (August 1, 2018) AN ENSEMBLE MODEL FOR COLLABORATIVE FILTERING TO INVOLVE ALL ASPECTS OF DATASET. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 60 2 15–26.
IEEE Y. Ar, “AN ENSEMBLE MODEL FOR COLLABORATIVE FILTERING TO INVOLVE ALL ASPECTS OF DATASET”, Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng., vol. 60, no. 2, pp. 15–26, 2018.
ISNAD Ar, YILMAZ. “AN ENSEMBLE MODEL FOR COLLABORATIVE FILTERING TO INVOLVE ALL ASPECTS OF DATASET”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 60/2 (August 2018), 15-26.
JAMA Ar Y. AN ENSEMBLE MODEL FOR COLLABORATIVE FILTERING TO INVOLVE ALL ASPECTS OF DATASET. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2018;60:15–26.
MLA Ar, YILMAZ. “AN ENSEMBLE MODEL FOR COLLABORATIVE FILTERING TO INVOLVE ALL ASPECTS OF DATASET”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, vol. 60, no. 2, 2018, pp. 15-26.
Vancouver Ar Y. AN ENSEMBLE MODEL FOR COLLABORATIVE FILTERING TO INVOLVE ALL ASPECTS OF DATASET. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2018;60(2):15-26.

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

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