Abstract
In this paper we present a neural learning-based clustering method for collaborative filtering. Collaborative filtering is an important task in recommender systems and has been investigated extensively in the past. Traditional approaches often require preprocessing steps, standard conditions or manually set gain. Our method is automatic, fast and robust towards cold start often seen in recommender systems. Furthermore, it can easily be trained to be used with any kind of data. The recommendation task is formulated as hybrid learning problem over two levels: artificial neural networks and clustering. Following the learning paradigm the detection on each level is performed by a trained classifier. First results of collaborative filtering using neural networks and clustering are presented and future additions are discussed.
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References
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Araniti, G., De Meo, P., Iera, A., Ursino, D.: Adaptively controlling the QoS of multimedia wireless applications through user profiling techniques. IEEE J. Sel. Areas Commun. 21(10), 1546–1556 (2003)
Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, SIAM, New Orleans, LA, pp. 1027–1035 (2007)
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison Wesley Longman, New York City (1999)
Bahmani, B., Moseley, B., Vattani, A., Kumar, R., Vassilvitskii, S.: Scalable k-means++. Proc. VLDB Endow. 5(7), 622–633 (2012)
Bishop, C.: Pattern Recognition and Machine Learning, 1st edn. Springer, New York (2006)
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl. Based Syst. 46, 109–132 (2013)
Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2012)
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper. In: Proceedings of ACM SIGIR Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, CA (1999)
Duda, R., Hart, P., Stork, D.: Pattern classification, 2nd edn. Wiley-Interscience, New York City (2000)
Friedman, N., Gieger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2–3), 131–163 (1997)
Henczel, S.: Creating user profiles to improve information quality. Factiva 28(3), 30 (2004)
Goldberg, T., Roeder, D., Gupta, C., Perkins, E.: A constant time collaborative filtering algorithm. Inf. Retrieval 4(2), 133–151 (2001)
Gong, S., Chongben, H.: Employing fuzzy clustering to alleviate the sparsity issue in collaborative filtering recommendation algorithms. In: Proceeding of 2008 International Pre-Olympic Congress on Computer Science, World Academic Press, Nanjing, China, pp. 449–454 (2008)
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann, Waltham (2011)
Isinkaye, F., Folajimi, Y., Ojokoh, B.: Recommendation systems: principles, methods and evaluation. Egypt. Inform. J. 16(3), 261–273 (2015)
Likas, A., Vlassis, N., Varbeek, J.: The global k-means clustering algorithm. Pattern Recogn. 36(2), 451–461 (2003)
Kuflik, T., Shoval, P.: Generation of user profiles for information filtering-research agenda. In: 23rd Annual International ACM SIGIR conference on Research and Development in Information Retrieval, Athens, Greece, pp. 313–315. ACM (2000)
Huang, Q.H., Ouyang, W.M.: Fuzzy collaborative filtering with multiple agents. J. Shanghai Univ. (English Edition) 11(3), 290–295 (2007)
Rumelhart, D., Hinton, G., Williams, R.: Learning Representations by Back? Propagating Errors. Nature 323, 533–536 (1986)
Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, Hong Kong, China, pp. 285–295. ACM (2001)
Su, X., Khoshgoftar, T.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 4, 1–19 (2009)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 3rd edn. Academic Press Inc, Orlando, FL (2006)
Witten, I., Frank, E., Hall, M., Pal, C.: Data Mining, 4th edn. Morgan Kaufmann, San Francisco, CA (2016)
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Mika, G.P., Dziczkowski, G. (2018). A Neural Learning-Based Clustering Model for Collaborative Filtering. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11055. Springer, Cham. https://doi.org/10.1007/978-3-319-98443-8_20
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DOI: https://doi.org/10.1007/978-3-319-98443-8_20
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