Concurrent reinforcement learning from customer interactions

30th International Conference on Machine Learning, ICML 2013

Volumn , Issue PART 3, 2013, Pages 1961-1969

  Silver, David ^a   Newnham, Leonard ^b   Barker, Dave ^b   Weller, Suzanne ^b   McFall, Jason ^b  

^a Causata Ltd   (United Kingdom)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CUSTOMER SATISFACTION; INDUSTRY; REINFORCEMENT LEARNING;

CUSTOMER INTERACTION; CUSTOMER LOYALTY; OBJECTIVE FUNCTIONS; PARTIAL INTERACTION; TEMPORAL DIFFERENCE LEARNING;

SALES;

EID: 84897527953     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Abe, N., Pednault, E., Wang, H., Zadrozny, B., Fan, W., and Apte, C. (2002). Empirical comparison of various reinforcement learning strategies for sequential targeted marketing. In International Conference on Data Mining, pages 3-10.
2. Abe, N., Verma, N., Schroko, R., and Apte, C. (2004). Cross channel optimized marketing by reinforcement learning. In International Conference on Knowledge Discovery and Data Mining (KDD), pages 767-772.
3. Archibald, T. (1992). Parallel dynamic programming. In Kronsjö, L. and Shumsheruddin, D., editors, Advances in parallel algorithms, pages 343-367. John Wiley & Sons, Inc.
4. Bertsekas, D. (1982). Distributed dynamic programming. Automatic Control, IEEE Transactions on, 27(3):610-616.
5. Gomez-Perez, G., Martin-Guerrero, J. D., Soria-Olivas, E., Balaguer-Ballester, E., Palomares, A., and Casariego, N. (2008). Assigning discounts in a marketing campaign by using reinforcement learning and neural networks. Expert Systems with Applications, (doi: 10.1016/j.eswa.2008.10.064).
6. Graepel, T., Candela, J. Q., Borchert, T., and Herbrich, R. (2010). Web-scale Bayesian click-through rate prediction for sponsored search advertising in microsoft's bing search engine. In 27th International Conference on Machine Learning, pages 13-20.
7. Grounds, M. and Kudenko, D. (2007). Parallel reinforcement learning with linear function approximation. In Adaptive Agents and Multi-Agent Systems, pages 60-74.
8. Kaelbling, L., Littman, M., and Cassandra, A. (1995). Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101:99-134. (Pubitemid 128387390)
9. Li, L., Chu, W., Langford, J., and Schapire, R. E. (2010). A contextual-bandit approach to personalized news article recommendation. CoRR, abs/1003.0146.
10. Littman, M. L. (1994). Markov games as a framework for multi-agent reinforcement learning. In 11th International Conference on Machine Learning, pages 157-163.
11. Pednault, E., Abe, N., Zadrozny, B., Wang, H., Fan, W., and Apte, C. (2002). Sequential cost-sensitive decision making with reinforcement learning. In International Conference on Knowledge Discovery and Data Mining (KDD).
12. Schraudolph, N. N. (1999). Local gain adaptation in stochastic gradient descent. In International Conference on Artificial Neural Networks, pages 569-574.
13. Sutton, R. (1988). Learning to predict by the method of temporal differences. Machine Learning, 3(9):9-44.
14. Sutton, R. (1992). Adapting bias by gradient descent: An incremental version of delta-bar-delta. In 10th National Conference on Artificial Intelligence, pages 171-176.
15. Sutton, R. and Barto, A. (1998). Reinforcement Learning: an Introduction. MIT Press.
16. Sutton, R., Precup, D., and Singh, S. (1999). Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence, 112(1-2):181-211. (Pubitemid 32079890)
17. Tsiptsis, K. and Chorianopoulos, A. (2010). Data Mining Techniques in CRM: Inside Customer Segmentation. Wiley.