Kolektywna klasteryzacja danych marketingowych - system rekomendacyjny Upsaily

• Autorzy: Jerzy Korczak , Maciej Pondel

Warianty tytułu

Collective Clustering of Marketing Data - Upsaily Recommendation System

• Języki publikacji: PL

Abstrakty

W artykule przedstawiono nowe algorytmicznie i technologicznie metody eksploracji danych w systemie rekomendacyjnym adresowanym do menedżerów sklepów internetowych. Poruszono temat wykorzystania algorytmów analizy skupień, inaczej klasteringu, wykorzystanych do segmentacji rynku. Na ogół wyniki grupowania przez indywidualne algorytmy różnią się miedzy sobą i mogą stwarzać problemy interpretacji i wykorzystania w zarządzaniu. Na podstawie analizy rezultatów działania kilku klasycznych algorytmów w zadaniu segmentacji metodą RFM (recency, frequency, monetary value) zaproponowano metodę kolektywnej klasteryzacji. Koncepcję i walory unifikacji algorytmów klasteringu zweryfikowano na dużej rzeczywistej bazie danych marketingowych. (abstrakt autora)

EN

The article presents new data mining methods in the recommendation system, addressed to online store managers. The article discusses the use of cluster analysis algorithms utilised to discover market segments. In general, the results of individual algorithms differ from one another, and can create problems with interpretation and management. Using the results of several classical algorithms, supported by the RFM segmentation, a method of collective clustering has been proposed. The concept and advantages of unifying the results of clustering the algorithms have been verified on a large, real marketing database. (original abstract)

Słowa kluczowe

PL

Klastry; Analiza skupień; Algorytmy; Handel elektroniczny; System rekomendujący; Profil konsumenta; Wyniki badań

EN

Business cluster; Cluster analysis; Algorithms; e-commerce; Recommender system; Customer profile; Research results

• Czasopismo: Przegląd Organizacji
• Rocznik: 2019
• Numer: nr 1
• Strony: 42--52

Twórcy

autor

Jerzy Korczak

• Międzynarodowa Wyższa Szkoła Logistyki i Transportu, Wrocław

autor

Maciej Pondel

• Uniwersytet Ekonomiczny we Wrocławiu

Bibliografia

• [1] Acilar A.M., Arslan A. (2019), A Collaborative Filtering Method Based on Artificial Immune Network, "Expert System Applications", Vol. 36, No. 4, pp. 8324-8332.
• [2] Adamovicius G., Tuzhilin A. (2005), Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions, "IEEE Trans, on Knowledge and Data Engineering", Vol. 17, No. 6, pp. 734-749.
• [3] Aggarval C.C. (2013), Data Clustering: Algorithms and Applications, Chapman & Hall, CRC Press, Boca Raton.
• [4] Ayad H" Kamel M.S. (2008), Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters,, "IEEE Transactions on Pattern Analysis and Machine Intelligence", Vol. 30, No. 1, pp. 160-173.
• [5] Balabanovic M., Shoham Y. (1997), Content-based, Collaborative Recommendation, "Communications of the ACM", Vol. 40, No. 3, pp. 66-72.
• [6] Beel ). (2015), Towards Efective Research-paper Recommender Systems and User Modeling Based on Mind Maps, PhD Thesis, Otto-von-Guericke Universitat Magdeburg.
• [7] Burke, R. (2002), Hybrid Recommender Systems: Survey and Experiments, "User Modeling User-Adaption Interaction", Vol. 12, No. 4, pp. 331-370.
• [8] Carmagnola F., Cena F., Gena C. (2011), User Model Interoperability: A survey, "User Modeling User-Adaption Interaction", Vol. 21, No. 3, pp. 285-331.
• [9] Cornuejols A., Wemmer C., Gancarski P., Bennani Y. (2017), Collaborative Clustering: Why, When, What and How, "Information Fusion", Vol. 39, pp. 81-95.
• [10] Chorianopoulos A. (2016), Effective CRM Using Predictive Analytics, John Wiley & Sons, Chichester.
• [11] Can G" Ma C" Wu J. (2007), Data Clustering: Theory, Algorithms, and Applications, SIAM Series.
• [12] Grira N., Crucianu M., Boujemaa N. (2008), Active Semi-supervised Fuzzy Clustering, "Pattern Recognition", Vol. 41, No. 5, pp. 1851-1861.
• [13] Faceli K., Ferreira de Carvalho A.C., Pereira de Souto M.G. (2007), Multiobjective Clustering Ensemble with Prior Knowledge, Vol. 4643, pp. 34-45, Springer.
• [14] Gancarski P., Cornueejols A., Wemmert C., Bennani Y. (2017), Clustering Collaboratif: Principes et Mise en Oeuvre, Proc. BDA'17, Nancy.
• [15] Ghodsi A. (2006), Dimensionality Reduction a Short Tutorial, Department of Statistics and Actuarial Science, Univ. of Waterloo, Ontario, Canada, 37, p. 38.
• [16] Goldberg D., Nichols D" Oki B.M., Terry D. (1992), Using Collaborative Filtering to Weave an Information Tapestry, "Communications of the ACM", Vol. 35, No. 12, pp. 61-70.
• [17] Gordon S., Linoff M" Berry J.A. (2011), Data Mining Techniques: For Marketing, Sales, and Customer Relationship, John Wiley & Sons, Chichester.
• [18] Jannach D., Zanker M., Felfernig A., Friedrich G. (2010), Recommender Systems - An Introduction, Cambridge University Press.
• [19] Jannach D., Zanker M., Ge M" Gróning M. (2012), Recommender Systems in Computer Science and Information Systems-a Landscape of Research, Proc. of the 13th International Conference, ЕС-Web, pp. 76-87.
• [20] Jordan M.I., Mitchell T.M. (2015), Machine Learning: Trends, Perspectives, and Prospects, "Science", Vol. 349, No. 6245, pp. 255-260.
• [21] Kashef R., Kamel M.S. (2010), Cooperative Clustering, "Pattern Recognition", Vol. 43, No. 6, pp. 2315-2329.
• [22] Kobiela E. (2011), Inteligentne systemy rekomendacyjne, Network Magazyn, http://www.networkmagazyn. pl/inteligentne-systemy-rekomendacji, data dostępu: 01.10.2018 r.
• [23] Konstan J.A., Riedl J. (2012), Recommender Systems: From Algorithms to User Experience, "User Modeling User-Adaption Interaction", Vol. 22, pp. 101-123.
• [24] Konstan J.A., Adomavicius G. (2013), Toward Identification and Adoption of Best Practices in Algorithmic Recommender Systems Research, Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation, pp. 23-28.
• [25] Law M.H., Topchy A., Jain A.K. (2004), Multiobjective Data Clustering, "IEEE Conference on Computer Vision and Pattern Recognition", Vol. 2, pp. 424-430.
• [26] Linoff G.S. (2015), Data Analysis Using SQL and Excel, John Wiley & Sons.
• [27] Mclnnes L., Healy J. (2018), UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, arXiv preprint arXiv: 1802.03426.
• [28] Nazemoff V. (2014), Customer Intelligence, [in:] The Four Intelligences of the Business Mind, Apress, Berkeley.
• [29] Nguyen N., Caruana R. (2007), Consensus Clusterings, International Conference on Data Mining, IEEE Computer Society, pp. 607-612.
• [30] Palopoli L., Rosaci D., Sarnę G.M. (2013), A Multitiered Recommender System Architecture for Supporting E-Commerce, [in:] G. Fortino, C. Badica, M. Malgeri, R. Unland (eds.) Intelligent Distributed Computing VI, Springer, pp. 71-81.
• [31] Pedrycz W. (2007), Collaborative and Knowledge-based Fuzzy Clustering, "International Journal of Innovative, Computing, Information and Control", Vol. 1, No. 3, pp. 1-12.
• [32] Quinlan J. (1996), Improved Use of Continuous Attributes in {C4.5I, "Journal of Artificial Intelligence Research", Vol. 4, pp. 77-90.
• [33] Resnick P., Varian H.R. (1997), Recommender Systems, "Communications of the ACM", Vol. 40, No. 3, pp. 56-58.
• [34] Ricci F., Rokach L., Shapira В., Kantor PB. (eds), (2011), Recommender Systems Handbook, Springer, pp. 1-35.
• [35] Sublemontier J.H. (2013), Unsupervised Collaborative Boosting of Clustering: A Unifying Framework for Multi-view Clustering, Multiple Consensus Clusterings and Alternative Clustering, International Joint Conference on Neural Networks.
• [36] Said A., Tikk D., Shi Y., Larson M., Stumpf K., Cremonesi P. (2012), Recommender Systems Evaluation: a 3d Benchmark, ACM RecSys 2012 Workshop on Recommendation Utility Evaluation: Beyond RMSE, pp. 21-23.
• [37] Wagstaff K., Cardie C., Rogers S., Schroedl S. (2001), Constrained K-means Clustering with Background Knowledge, International Conference on Machine Learning, pp. 557-584.
• [38] Wemmert C., Gancarski P., Korczak J. (2000), A Collaborative Approach to Combine Multiple Learning Methods, "International Journal on Artificial Intelligence Tools (World Scientific)", Vol. 9, No. 1, pp. 59-78.

Identyfikatory

DOI

10.33141/po.2019.01.06