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2017 | OriginalPaper | Buchkapitel

Scalable Online Top-N Recommender Systems

verfasst von : Alípio M. Jorge, João Vinagre, Marcos Domingues, João Gama, Carlos Soares, Pawel Matuszyk, Myra Spiliopoulou

Erschienen in: E-Commerce and Web Technologies

Verlag: Springer International Publishing

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Abstract

Given the large volumes and dynamics of data that recommender systems currently have to deal with, we look at online stream based approaches that are able to cope with high throughput observations. In this paper we describe work on incremental neighborhood based and incremental matrix factorization approaches for binary ratings, starting with a general introduction, looking at various approaches and describing existing enhancements. We refer to recent work on forgetting techniques and multidimensional recommendation. We will also focus on adequate procedures for the evaluation of online recommender algorithms.

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Nächstes Kapitel User Control in Recommender Systems: Overview and Interaction Challenges
Fußnoten
2
The term sensitivity is used here with its broader meaning, not as a synonym of recall.
 
Literatur
1.
Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Pu, P., Bridge, D.G., Mobasher, B., Ricci, F. (eds.) Proceedings of the 2008 ACM Conference on Recommender Systems, Rec-Sys 2008, Lausanne, 23–25 October 2008, pp. 335–336 (2008)
2.
3.
Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. JASIS 41(6), 391–407 (1990)CrossRef
4.
Diaz-Aviles, E., Drumond, L., Schmidt-Thieme, L., Nejdl, W.: Real-time top-N recommendation in social streams. In: Cunningham, P., Hurley, N.J., Guy, I., Anand, S.S. (eds.) Sixth ACM Conference on Recommender Systems, RecSys 2012, Dublin, 9–13 September, pp. 59–66. ACM (2012)
5.
Domingos, P.M., Hulten, G.: Mining high-speed data streams. In: Ramakrishnan, R., Stolfo, S.J., Bayardo, R.J., Parsa, I. (eds.) Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, 20–23 August, pp. 71–80. ACM (2000)
6.
Domingues, M.A., Gouyon, F., Jorge, A.M., Leal, J.P., Vinagre, J., Lemos, L., Sordo, M.: Combining usage, content in an online recommendation system for music in the long tail. IJMIR 2(1), 3–13 (2013)
7.
Domingues, M.A., Jorge, A.M., Soares, C.: Dimensions as virtual items: improving the predictive ability of top-N recommender systems. Inf. Process. Manag. 49(3), 698–720 (2013)CrossRef
8.
Duan, L., Street, W.N., Xu, E.: Healthcare information systems: data mining methods in the creation of a clinical recommender system. Enterp. Inf. Syst. 5(2), 169–181 (2011)CrossRef
9.
10.
11.
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Li, Y., Liu, B., Sarawagi, S. (eds.) Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, 24–27 August, pp. 426–434. ACM (2008)
12.
LeCun, Y.A., Bottou, L., Orr, G.B., Müller, K.-R.: Efficient backprop. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 9–48. Springer, Heidelberg (2012). doi:10.​1007/​978-3-642-35289-8_​3 CrossRef
13.
Lee, D., Hosanagar, K.: When do recommender systems work the best? The moderating effects of product attributes and consumer reviews on recommender performance. In: Bourdeau, J., Hendler, J., Nkambou, R., Horrocks, I., Zhao, B.Y. (eds.) Proceedings of the 25th International Conference on World Wide Web, WWW, Montreal, 11–15 April, pp. 85–97. ACM (2016)
14.
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRef
15.
Manouselis, N., Drachsler, H., Verbert, K., Duval, E.: Recommender Systems for Learning. Springer Briefs in Electrical and Computer Engineering. Springer, New York (2013)CrossRef
16.
Matuszyk, P., Vinagre, J., Spiliopoulou, M., Jorge, A.M., Gama, J.: Forgetting methods for incremental matrix factorization in recommender systems. In: Wainwright et al. [35], pp. 947–953
17.
McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: Olson, G.M., Jeffries, R. (eds.) Extended Abstracts Proceedings of the Conference on Human Factors in Computing Systems, CHI, Montréal, 22–27 April, pp. 1097–1101. ACM (2006)
18.
Miranda, C., Jorge, A.M.: Incremental collaborative filtering for binary ratings. In: 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008, 9–12 December 2008, Sydney, Main Conference Proceedings, pp. 389–392. IEEE Computer Society (2008)
19.
Moreira-Matias, L., Fernandes, R., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L.: On recommending urban hotspots to find our next passenger. In: Gama, J., May, M., Marques, N.C., Cortez, P., Ferreira, C.A. (eds.) Proceedings of the 3rd Workshop on Ubiquitous Data Mining Co-located with the 23rd International Joint Conference on Artificial Intelligence (IJCAI ), Beijing, 3 August. CEUR Workshop Proceedings, vol. 1088, p. 17. CEUR-WS.org (2013)
20.
Nguyen, T.T., Hui, P.-M., Harper, F.M., Terveen, L., Konstan, J.A.: Exploring the filter bubble. In: Proceedings of the 23rd International Conference on World Wide Web - WWW 2014, pp. 677–686 (2014)
21.
Papagelis, M., Rousidis, I., Plexousakis, D., Theoharopoulos, E.: Incremental collaborative filtering for highly-scalable recommendation algorithms. In: Hacid, M.-S., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS (LNAI), vol. 3488, pp. 553–561. Springer, Heidelberg (2005). doi:10.​1007/​11425274_​57 CrossRef
22.
Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD Cup and Workshop 2007, pp. 5–8 (2007)
23.
Pearl, P., Chen, L., Rong, H.: Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Model. User Adapt. Interact. 22(4), 317–355 (2012)
24.
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Bilmes, J.A., Ng, A.Y. (eds.) Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2009, Montreal, 18–21 June, pp. 452–461. AUAI Press (2009)
25.
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook. Springer, New York (2011)MATH
26.
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Platt, J.C., Koller, D., Singer, Y., Roweis, S.T. (eds.) Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, 3–6 December, pp. 1257–1264. Curran Associates Inc. (2007)
27.
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Shen, V.Y., Saito, N., Lyu, M.R., Zurko, M.E. (eds.) Proceedings of the Tenth International World Wide Web Conference, WWW 2010, Hong Kong, 1–5 May, pp. 285–295. ACM (2001)
28.
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.T.: Application of dimensionality reduction in recommender system - a case study. In: WEBKDD 2000: Web Mining for E-Commerce - Challenges and Opportunities, 20 August, Boston (2000)
29.
Takács, G., Pilászy, I., Németh, B., Tikk, D.: Scalable collaborative filtering approaches for large recommender systems. J. Mach. Learn. Res. 10, 623–656 (2009)
30.
Veloso, M., Jorge, A., Azevedo, P.J.: Model-based collaborative filtering for team building support. ICEIS (2), 241–248 (2004)
31.
Vinagre, J., Jorge, A.M.: Forgetting mechanisms for scalable collaborative filtering. J. Braz. Comput. Soc. 18(4), 271–282 (2012)CrossRef
32.
Vinagre, J., Jorge, A.M., Gama, J.: Fast incremental matrix factorization for recommendation with positive-only feedback. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) UMAP 2014. LNCS, vol. 8538, pp. 459–470. Springer, Heidelberg (2014). doi:10.​1007/​978-3-319-08786-3_​41
33.
Vinagre, J., Jorge, A.M., Gama, J.: Collaborative filtering with recency-based negative feedback. In: Wainwright et al. [34], pp. 963–965
34.
Wainwright, R.L., Corchado, J.M., Bechini, A., Hong, J. (eds.): Proceedings of the 30th Annual ACM Symposium on Applied Computing, Salamanca, 13–17 April. ACM (2015)
Metadaten
Titel
Scalable Online Top-N Recommender Systems
verfasst von
Alípio M. Jorge
João Vinagre
Marcos Domingues
João Gama
Carlos Soares
Pawel Matuszyk
Myra Spiliopoulou
Copyright-Jahr
2017
Buch
E-Commerce and Web Technologies

Print ISBN: 978-3-319-53675-0

Electronic ISBN: 978-3-319-53676-7

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-53676-7_1