Products and services recommendation systems in e-commerce. Recommendation methods, algorithms, and measures of their effectiveness

• Authors: Mirosława Lasek , Dominik Kosieradzki
• Languages of publication: EN

Abstracts

EN

The article concerns products and services recommendation systems in ecommerce which have become increasingly important for both consumers and retailers. The methods used for the recommendation of products and services, as well as the algorithms used to implement them, are presented in the article. Particular attention was paid to the problems of testing the suitability of algorithms, along with the effectiveness measures of the applications of the methods and algorithms.

Keywords

EN

recommendation systems; recommender systems; recommendation methods; recommendation algorithms; collaborative filtering; content-based filtering; memory-based algorithms; model-based algorithms; predictive accuracy metrics; classification accuracy metrics

• Publisher: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
• Journal: Informatyka Ekonomiczna
• Year: 2014
• Issue: 1(31)
• Pages: 304-317

Contributors

author

Mirosława Lasek

author

Dominik Kosieradzki

References

• Amatriain X., Jaimes A., Oliver N., Pujol J.M., 2011, Chapter 2: Data mining Methods for Recommender Systems, [in:] F. Ricci, L. Rokach, B. Shapira, P.B. Kantor (eds.), Recommender systems Handbook, Springer Science+Business Media LLC,.
• Anderson C., 2004, The Long Tail, Wired 12.10, October.
• Breese J., Heckerman D., Kadie C., 1998, Empirical analysis of predictive algorithms for collaborative filtering, Microsoft Research, Redmond, USA, September.
• Burke R., 2002, Hybrid recommender systems: Survey and Experiment, “User Modeling and User-Adapted Interaction”, no. 12(4).
• Gediminas A., 2005, Towards the Next Generation of Recommender Systems: A Survey of the Stateof-the-Art and Possible Extension, “IEEE Transactions on Knowledge and Data Engineering”, vol. 17, no. 6, June.
• Herlocker J., Konstan J., Terveen L., Riedl J., 2004, Evaluating Collaborative Filtering Recommender Systems, “ACM Transactions on Information Systems”, vol. 22, no. 1, January.
• Hernandez del Olmo F., Gaudioso E., 2008, Evaluation of recommender systems: A new approach, “Expert Systems with Applications”, vol. 35, Issue 3, October.
• Koren Y., Bell R., Volinsky C., 2009, Matrix factorization techniques for recommender systems, “IEEE Computer Society”, 0018-9162/09.
• Kosieradzki D., 2013, Analysis and an overview of methods used for building, implementing and testing recommender systems on the electronic market, Faculty of Economic Sciences, University of Warsaw, Warsaw.
• Kwon H.J., Lee T.H., Hong K.S., 2009, Improved Memory-based Collaborative Filtering Using Entropy-based Similarity Measures, Proceedings of the 2009 International Symposium on Web Information Systems and Applications (WISA’09), Nanchang, China, 22-24 May.
• Martin F.J., Donaldson J., Ashenfelter A., Torrens M., Hangartner R., 2011, The Big Promise of Recommender Systems, “AI Magazine”, vol. 32, September.
• Matusiak K. et al., 2011, Innowacje i transfer technologii. Słownik pojęć, PARP, wydanie III, Warszawa.
• Melville P., Sindhwani V., 2010, Recommender Systems, Encyclopedia of Machine Learning, Chapter No: 00338, April.
• Resnick P., Varian H.R., 1997, Recommender Systems, “Communications of the ACM”, no. 40(3).
• Retail/E-Commerce Industry Report Q4 2011, iPerceptions Inc., 2012, http://www.iperceptions.com/files/Retail-E-Commerce-Industry-Report-Q4-2011-FINAL2.pdf (2.12. 2012).
• Ricci F., Rokach L., Shapira B., Kantor P.B., 2011, Chapter 1: Introduction to Recommender Systems Handbook, [in:] F. Ricci et al. (eds.), Recommender systems Handbook, Springer Science+Business Media LLC.
• Schonfeld E., Click here for the upsell, CNN Money, 11 July 2007, http://money.cnn.com/magazines/business2/business2_archive/2007/07/01/100117056 (18.11.2012).
• Schrage M., 2008, Recommendation nation, MIT Technology Review, May-June.
• Schroeder G., Thiele M., Lehner W., 2011, Setting Goals and Choosing Metrics for Recommender System Evaluations, UCERSTI2 Workshop at the 5th ACM Conference on Recommender Systems, Chicago, USA, 23 October 2011.
• Su X., Khoshgoftaar T.M., 2009, A Survey of Collaborative Filtering Techniques, Advances in Artificial Intelligence, vol. 2009.