Machine Learning as a method of adapting offers to the clients
Jacek Bielecki
jacek.bielecki@pollub.edu.plLublin University of Technology (Poland)
Oskar Ceglarski
Lublin University of Technology (Poland)
Maria Skublewska-Paszkowska
Lublin University of Technology (Poland)
Abstract
Recommendation systems are class of information filter applications whose main goal is to provide personalized recommendations. The main goal of the research was to compare two ways of creating personalized recommendations. The recommendation system was built on the basis of a content-based cognitive filtering method and on the basis of a collaborative filtering method based on user ratings. The conclusions of the research show the advantages and disadvantages of both methods.
Keywords:
recommender system; collaborative filtering; cognitive filtering; machine learningReferences
[1] M. Beladev, L. Rokach, B. Shapira, Recommender systems for product bundling, Knowledge-Based Systems, 1 Nov. 2016.
[2] A. Marwade, N. Kumar, S. Mundada, Augmenting E-Commerce Product Recommendations by Analyzing Customer Personality, Conference: 9th International Conference on Computational Intelligence and Communication Networks (CICN); 16-17 Sep. 2017.
[3] J. Bobadilla, Recommender systems survey, Knowledge-Based Systems, 46 (2013), 109-132.
[4] S. Jiang, Q. Xueming, S. Jialie, F. Yun, Author topic model-based collaborative filtering for personalized POI recommendations, IEEE transactions on multimedia 17:6 (2015), 907-918.
[5] R. Burke. Hybrid recommender systems: survey and experiments, UMUAI, 12 (4) (2002), 331-370
[6] S. Bag, SK. Kumar, MK. Tiwari, An efficient recommendation generation using relevant Jaccard similarity, Information Sciences, May 2019.
[7] D. McIlwraith, M. Haralambos, B. Dmitry, Inteligentna sieć, Algorytmy przyszłości. Helion, Gliwice, 2017.
[8] Natural Language Toolkit, https://www.nltk.org/api/nltk.html.
[9] F. Xie , J. Wang , R. Xiong , N. Zhang, An Integrated Service Recommendation Approach for Service-Based System Development, Expert Systems With Applications, 2019.
[10] B. K. Patra, R. Launonen, V. Ollikainen, S. Nandi, A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data, Knowledge-Based Syst. 82 (2015) 163–177.
[11] H. J. Ahn, A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem, Inf. Sci. (Ny). 178 (2008) 37–51.
[12] M. Saeed, E.G. Mansoori, A new slope one based recommendation algorithm using virtual predictive items, Journal of Intelligent Information Systems, June 2018, Volume 50, Issue 3, pp 527–547
[13] D. Lemire, A. Maclachlan, Slope One predictors for online rating-based collaborative filtering, SDM. SIAM, 2005 (Vol. 5 pp. 1–5)
[14] W. Yongqiang, Y. Liang, C. Bing, Learning to Recommend Based on Slope One Strategy, Web Technologies and Applications. Proceedings of the 14th Asia-Pacific Web Conference, APWeb 2012 pp: 537-4.
[15] QX. Wang, X. Luo, Y. Li, Incremental Slope-one recommenders, Neurocomputing, Volume 272, Journal of Computer Sciences Institute, 10 January 2018, pp 606-618
[16] X. Luo., Y.-N. Xia, Q. Zhu, Incremental collaborative filtering recommender based on regularized matrix factorization, Knowl. Based Syst., 27 (2012), pp. 271-280
[17] X. Luo., Y.-N. Xia, Q. Zhu, Y. Li, Boosting the K-nearest-neighborhood based incremental collaborative filtering, Knowl. Based Syst., 53 (2013), pp. 90-99
[18] Nguyen P., Wang J., Kalousis A.: Factorizing LambdaMART for cold start recommendations. Machine Learning, 21 July 2016
[19] T. Schreiner, A. Rese, D. Baie, Multichannel personalization: Identifying consumer preferences for product recommendations in advertisements across different media channels, Journal of Retailing and Consumer Services, May 2019
[20] A. Fiasconaro, M. Tumminello, V. Nicosia, V. Latora, R. N. Mantegna, Hybrid recommendation methods in complex networks. American Physical Society, 14 July 2015.
[2] A. Marwade, N. Kumar, S. Mundada, Augmenting E-Commerce Product Recommendations by Analyzing Customer Personality, Conference: 9th International Conference on Computational Intelligence and Communication Networks (CICN); 16-17 Sep. 2017.
[3] J. Bobadilla, Recommender systems survey, Knowledge-Based Systems, 46 (2013), 109-132.
[4] S. Jiang, Q. Xueming, S. Jialie, F. Yun, Author topic model-based collaborative filtering for personalized POI recommendations, IEEE transactions on multimedia 17:6 (2015), 907-918.
[5] R. Burke. Hybrid recommender systems: survey and experiments, UMUAI, 12 (4) (2002), 331-370
[6] S. Bag, SK. Kumar, MK. Tiwari, An efficient recommendation generation using relevant Jaccard similarity, Information Sciences, May 2019.
[7] D. McIlwraith, M. Haralambos, B. Dmitry, Inteligentna sieć, Algorytmy przyszłości. Helion, Gliwice, 2017.
[8] Natural Language Toolkit, https://www.nltk.org/api/nltk.html.
[9] F. Xie , J. Wang , R. Xiong , N. Zhang, An Integrated Service Recommendation Approach for Service-Based System Development, Expert Systems With Applications, 2019.
[10] B. K. Patra, R. Launonen, V. Ollikainen, S. Nandi, A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data, Knowledge-Based Syst. 82 (2015) 163–177.
[11] H. J. Ahn, A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem, Inf. Sci. (Ny). 178 (2008) 37–51.
[12] M. Saeed, E.G. Mansoori, A new slope one based recommendation algorithm using virtual predictive items, Journal of Intelligent Information Systems, June 2018, Volume 50, Issue 3, pp 527–547
[13] D. Lemire, A. Maclachlan, Slope One predictors for online rating-based collaborative filtering, SDM. SIAM, 2005 (Vol. 5 pp. 1–5)
[14] W. Yongqiang, Y. Liang, C. Bing, Learning to Recommend Based on Slope One Strategy, Web Technologies and Applications. Proceedings of the 14th Asia-Pacific Web Conference, APWeb 2012 pp: 537-4.
[15] QX. Wang, X. Luo, Y. Li, Incremental Slope-one recommenders, Neurocomputing, Volume 272, Journal of Computer Sciences Institute, 10 January 2018, pp 606-618
[16] X. Luo., Y.-N. Xia, Q. Zhu, Incremental collaborative filtering recommender based on regularized matrix factorization, Knowl. Based Syst., 27 (2012), pp. 271-280
[17] X. Luo., Y.-N. Xia, Q. Zhu, Y. Li, Boosting the K-nearest-neighborhood based incremental collaborative filtering, Knowl. Based Syst., 53 (2013), pp. 90-99
[18] Nguyen P., Wang J., Kalousis A.: Factorizing LambdaMART for cold start recommendations. Machine Learning, 21 July 2016
[19] T. Schreiner, A. Rese, D. Baie, Multichannel personalization: Identifying consumer preferences for product recommendations in advertisements across different media channels, Journal of Retailing and Consumer Services, May 2019
[20] A. Fiasconaro, M. Tumminello, V. Nicosia, V. Latora, R. N. Mantegna, Hybrid recommendation methods in complex networks. American Physical Society, 14 July 2015.
Bielecki, J., Ceglarski, O., & Skublewska-Paszkowska, M. (2019). Machine Learning as a method of adapting offers to the clients. Journal of Computer Sciences Institute, 13, 267–271. https://doi.org/10.35784/jcsi.1293
Authors
Oskar CeglarskiLublin University of Technology Poland
Authors
Maria Skublewska-PaszkowskaLublin University of Technology Poland
Statistics
Abstract views: 380PDF downloads: 244
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
