Abstract
Building a recommendation system to withstand the rapid change in items’ relevance to users is a challenge requiring continual optimization. In a Big Data scenario, it becomes a harder problem, in which users get substantially diverse in their tastes. We propose an algorithm that is based on the UBC1 bandit algorithm to cover a large variety of users. To enhance UCB1, we designed a new rewarding scheme to encourage the bandits to choose items that satisfy a large number of users. Our approach takes account of the correlation among the items preferred by different types of users, in effect, increasing the coverage of the recommendation set efficiently. Our method performs better than existing techniques such as Ranked Bandits [8] and Independent Bandits [6] in terms of satisfying diverse types of users.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agrawal, S.: Optimization under uncertainty: Bounding the correlation gap. Ph.D. thesis, March 2011. http://research.microsoft.com/apps/pubs/default.aspx?id=200425
Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47(2–3), 235–256 (2002). http://dx.doi.org/10.1023/A:1013689704352
Burke, R.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007). http://dl.acm.org/citation. cfm?id=1768197.1768211
Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends Hum.-Comput. Interact. 4(2), 81–173 (2011). http://dx.doi.org/10.1561/1100000009
Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A constant time collaborative filtering algorithm. Inf. Retr. 4(2), 133–151 (2001). http://dx.doi.org/10.1023/A:1011419012209
Kohli, P., Salek, M., Stoddard, G.: A fast bandit algorithm for recommendation to users with heterogenous tastes. In: desJardins, M., Littman, M.L. (eds.) AAAI. AAAI Press (2013). http://dblp.uni-trier.de/db/conf/aaai/aaai2013.html#KohliSS13
MovieLens dataset. http://www.grouplens.org/data/ (as of 2003). http://www.grouplens.org/data/
Radlinski, F., Kleinberg, R., Joachims, T.: Learning diverse rankings with multi-armed bandits. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pp. 784–791. ACM, New York (2008). http://doi.acm.org/10.1145/1390156.1390255
Robertson, S.E.: The probability ranking principle in IR. In: Readings in Information Retrieval, pp. 281–286. Morgan Kaufmann Publishers Inc., San Francisco (1997). http://dl.acm.org/citation.cfm?id=275537.275701
Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Recommender Systems Handbook, pp. 257–297 (2011). http://scholar.google.de/scholar.bib?q=info:AW2lmZl44hMJ:scholar.google.com/&output=citation&hl=de&as_sdt=0,5&ct=citation&cd=0
Vermorel, J., Mohri, M.: Multi-armed bandit algorithms and empirical evaluation. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 437–448. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Rahman, M., Oh, J.C. (2015). Fast Online Learning to Recommend a Diverse Set from Big Data. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-19066-2_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19065-5
Online ISBN: 978-3-319-19066-2
eBook Packages: Computer ScienceComputer Science (R0)