Learning Adaptive Recommendation Strategies for Online Travel Planning
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Conversational recommender systems support human-computer interaction strategies in order to assist online tourists in the important activity of dynamic packaging, i.e., in building personalized travel plans and in booking their holidays. In a previous paper, we presented a novel recommendation methodology based on Reinforcement Learning, which allows conversational systems to autonomously improve a rigid (non-adaptive) strategy in order to learn an optimal (adaptive) one. We applied our approach within an online travel recommender system, which is supported by the Austrian Tourism portal (Austria.info). In this paper, we present the results of this online evaluation. We show that the optimal strategy adapts its actions to the served users, and deviates from a rigid default strategy. More importantly, we show that the optimal strategy is able to assist online tourists in acquiring their goals more efficiently than the rigid strategy, and is able to increase the willingness of the users in accepting several of the system’s offers.
KeywordsConversational Recommender Systems Reinforcement Learning Markov Decision Process Travel Planning Dynamic Packaging Information Presentation and Delivery
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- Bridge, D., Goker, M., McGinty, L., and Smyth, B. (2006). Case-based recommender systems. The Knowledge Engineering review, 20(3), 315–320.Google Scholar
- Fesenmaier, D. R., Werthner, H., and Woeber, K. (2006). Destination Recommendation Systems: Behavioural Foundations and Applications. CABI Publishing.Google Scholar
- Katz, M.A. (2001). Searching and Browsing on E-commerce Sites: Frequency, Efficiency and Rationale. Doctoral disseration, Rice University, Houston, TX.Google Scholar
- Mahmood, T. and Ricci, F. (2007a). Learning and adaptivity in interactive recommender systems. In Proceedings of the ICEC’07 Conference, Minneapolis, USA, 75–84.Google Scholar
- Mahmood, T., Cavada, D., Ricci, F., and Venturini, A. (2007b). Search and recommendation functionality. Technical Report D5.1, eTourism Competence Center Austria, Technikerstr. 21a, ICT-Technologiepark, 6020 Innsbruck.Google Scholar
- Mahmood, T. and Ricci, F. (2008). Adapting the Interaction State Model in Conversational Recommender Systsems. In Proceedings of the ICEC’08 Conference, Innsbruck, Austria, 1–10.Google Scholar
- Mahmood, T., Ricci, F., Venturini, A., and Höpken, W. (2008). Adaptive recommender systems for travel planning. In O’Connor, P., Höpken, W., and Gretzel, U. (eds), Information and Communication Technologies in Tourism 2008, proceedings of ENTER 2008 International Conference, Innsbruck, 2008, Springer, 1–11.Google Scholar
- Nielsen, J. (1993). Usability engineering. San Francisco: Morgan Kaufmann Publisher.Google Scholar
- Sutton, R. S. and Barto, A. G. (1998). Reinforcement Learning: An Introduction. MIT Press.Google Scholar
- Venturini, A. and Ricci, F. (2006). Applying trip@dvice recommendation technology to www.visiteurope.com. In Proceedings of the 17th European Conference on Artificial Intelligence, Riva del Garda, Italy, Aug 28th–Sept 1st, 607–611.Google Scholar
- Zanker, M, Fuchs, M., Höpken, W., Tuta, M., and Müller, N. Evaluating Recommender Systems in Tourism—A Case Study from Austria. In Information and Communication Technologies in Tourism 2008, proceedings of ENTER 2008 International Conference, Innsbruck, 2008. Springer, 24–34.Google Scholar