Abstract
Recommending best-fit rate-plans for new users is a challenge for the Telco industry. Rate-plans differ from most traditional products in the way that a user normally only have one product at any given time. This, combined with no background knowledge on new users hinders traditional recommender systems. Many Telcos today use either trivial approaches, such as picking a random plan or the most common plan in use. The work presented here shows that these methods perform poorly. We propose a new approach based on the multi-armed bandit algorithms to automatically recommend rate-plans for new users. An experiment is conducted on two different real-world datasets from two brands of a major international Telco operator showing promising results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Park, S.T., Pennock, D., Madani, O., Good, N., DeCoste, D.: Naïve filterbots for robust cold-start recommendations. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 699–705 (2006)
Adomavicius, G., Tuzhilin, A.: Towards the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17, 734–749 (2005)
Massa, P., Bhattacharjee, B.: Using trust in recommender systems: An experimental analysis. In: Jensen, C., Poslad, S., Dimitrakos, T. (eds.) iTrust 2004. LNCS, vol. 2995, pp. 221–235. Springer, Heidelberg (2004)
Burke, R.: Hybrid recommender systems: Survey and experiments. user modeling and user-adapted interaction. User Modeling and User-Adapted Interaction 12, 331–370 (2002)
Lai, T.L., Robbins, H.: Asymptotically efficient adaptive allocation rules. Advances in Applied Mathematics 6, 4–22 (1985)
Katehakis, M., Veinott, J.A.: The multi-armed bandit problem: decomposition and computation. Mathematics of Operations Research 12, 262–268 (1987)
Auer, P., Cesa-Bainchi, M., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Machine Learning 47, 235–256 (2002)
Thomas, S., Wilson, J., Chaudhury, S.: Best-fit mobile recharge pack recommendation. In: National Conference on Communications (NCC), pp. 1–5 (2013)
Soonsiripanichkul, B., Tongtep, N., Theeramunkong, T.: Mobile package recommendation using classification with feature discretization and threshold-based ensemble technique. In: Proceedings of the International Conference on Information and Communication Technology for Embedded Systems, ICICTES 2014 (2014)
Lekakos, G., Giaglis, G.M.: A hybrid approach for improving predictive accuracy of collaborative filtering algorithms. User Modeling and User-Adapted Interaction 17, 5–40 (2007)
Lam, X.N., Vu, T., Le, T.D., Duong, A.D.: Addressing cold-start problem in recommendation systems. In: Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, pp. 208–211. ACM (2008)
Marlin, B.: Collaborative filtering: A machine learning perspective. Technical report, University of Toronto (2004)
Gao, F., Xing, C., Du, X., Wang, S.: Personalized service system based on hybrid filtering for digital library. Tsinghua Science & Technology 12, 1–8 (2007)
Agarwal, D., Chen, B.C.: Regression-based latent factor models. In: KDD 2009: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM SIGKDD, pp. 19–28. ACM (2009)
Park, S.T., Chu, W.: Pairwise preference regression for cold-start recommendation. In: RecSys 2009: Proceedings of ACM conference on Recommender systems, pp. 21–28 (2009)
Zigoris, P., Zhang, Y.: Bayesian adaptive user profiling with explicit & implicit feedback. In: CIKM 2006: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp. 397–404. ACM (2006)
Manavoglu, E., Pavlov, D., Giles, C.L.: Probabilistic user behavior models. In: ICDM 2003: Proceedings of Third IEEE International Conference on Data Mining (2003)
Xue, G.R., Han, J., Yu, Y., Yang, Q.: User language model for collaborative personalized search. ACM Transactions on Information Systems 27, 1–28 (2009)
Auer, P., Cesa-Bianchi, N., Freund, Y., Schapire, R.E.: The nonstochastic multiarmed bandit problem. SIAM Journal on Computing 32, 48–77 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Nguyen, H.T., Kofod-Petersen, A. (2014). Using Multi-armed Bandit to Solve Cold-Start Problems in Recommender Systems at Telco. In: Prasath, R., O’Reilly, P., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8891. Springer, Cham. https://doi.org/10.1007/978-3-319-13817-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-13817-6_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13816-9
Online ISBN: 978-3-319-13817-6
eBook Packages: Computer ScienceComputer Science (R0)