Abstract
In this paper, we propose a framework based on Hierarchical Reinforcement Learning for dialogue management in a Conversational Recommender System scenario. The framework splits the dialogue into more manageable tasks whose achievement corresponds to goals of the dialogue with the user. The framework consists of a meta-controller, which receives the user utterance and understands which goal should pursue, and a controller, which exploits a goal-specific representation to generate an answer composed by a sequence of tokens. The modules are trained using a two-stage strategy based on a preliminary Supervised Learning stage and a successive Reinforcement Learning stage.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
NLTK word tokenizer documentation: https://goo.gl/L4Y1Rc.
References
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)
Bellman, R.: A Markovian decision process. Technical report, DTIC Document (1957)
Benito-Picazo, F., Enciso, M., Rossi, C., Guevara, A.: Conversational recommendation to avoid the cold-start problem. In: Proceedings of the 16th International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE 2016 (2016)
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., Zaremba, W.: OpenAI gym (2016)
Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level BLEU. In: ACL 2014, p. 362 (2014)
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Christakopoulou, K., Radlinski, F., Hofmann, K.: Towards conversational recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 815–824. ACM, New York (2016)
Dooms, S., De Pessemier, T., Martens, L.: Movietweetings: a movie rating dataset collected from Twitter. In: Workshop on Crowdsourcing and Human Computation for Recommender Systems, CrowdRec at RecSys, vol. 2013, p. 43 (2013)
Greco, C., Suglia, A., Basile, P., Rossiello, G., Semeraro, G.: Iterative multi-document neural attention for multiple answer prediction. In: Proceedings of the AI*IA Workshop on Deep Understanding and Reasoning: A Challenge for Next-generation Intelligent Agents 2016 co-located with 15th International Conference of the Italian Association for Artificial Intelligence (AIxIA 2016), Genova, Italy, 28 November 2016, pp. 19–29 (2016)
Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. (TiiS) 5(4), 19 (2016)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kulkarni, T.D., Narasimhan, K., Saeedi, A., Tenenbaum, J.: Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation. In: Advances in Neural Information Processing Systems, pp. 3675–3683 (2016)
Li, J., Galley, M., Brockett, C., Spithourakis, G.P., Gao, J., Dolan, B.: A persona-based neural conversation model. arXiv preprint arXiv:1603.06155 (2016)
Lin, C.Y., Och, F.J.: Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, p. 605. Association for Computational Linguistics (2004)
Mahmood, T., Ricci, F.: Learning and adaptivity in interactive recommender systems. In: Proceedings of the Ninth International Conference on Electronic Commerce, pp. 75–84. ACM (2007)
Mahmood, T., Ricci, F.: Adapting the interaction state model in conversational recommender systems. In: Proceedings of the 10th International Conference on Electronic Commerce, p. 33. ACM (2008)
Mahmood, T., Ricci, F.: Improving recommender systems with adaptive conversational strategies. In: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, pp. 73–82. ACM (2009)
Mahmood, T., Ricci, F., Venturini, A., Höpken, W.: Adaptive recommender systems for travel planning. Inf. Commun. Technol. Tour. 2008, 1–11 (2008)
Maisto, D., Donnarumma, F., Pezzulo, G.: Divide et impera: subgoaling reduces the complexity of probabilistic inference and problem solving. J. R. Soc. Interface 12(104), 20141335 (2015)
Mc Ginty, L., Smyth, B.: Deep dialogue vs casual conversation in recommender systems (2002)
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311–318. Association for Computational Linguistics (2002)
Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: ICML, vol. 3, no. 28, pp. 1310–1318 (2013)
Ricci, F., Nguyen, Q.N.: Acquiring and revising preferences in a critique-based mobile recommender system. IEEE Intell. Syst. 22(3), 22–29 (2007)
Rubens, N., Elahi, M., Sugiyama, M., Kaplan, D.: Active learning in recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 809–846. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_24
Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)
Sordoni, A., Bachman, P., Trischler, A., Bengio, Y.: Iterative alternating neural attention for machine reading. arXiv preprint arXiv:1606.02245 (2016)
Suglia, A., Greco, C., Basile, P., Semeraro, G., Caputo, A.: An automatic procedure for generating datasets for conversational recommender systems. In: Proceedings of Dynamic Search for Complex Tasks - 8th International Conference of the CLEF Association, CLEF 2017, Dublin, Ireland, 11–14 September 2017 (2017)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
Sutton, R.S., Precup, D., Singh, S.: Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning. Artif. Intell. 112(1), 181–211 (1999)
Vinyals, O., Le, Q.: A neural conversational model. arXiv preprint arXiv:1506.05869 (2015)
Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8(3–4), 229–256 (1992)
Williams, R.J., Peng, J.: Function optimization using connectionist reinforcement learning algorithms. Conn. Sci. 3(3), 241–268 (1991)
Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al.: Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)
Zaremba, W., Sutskever, I.: Reinforcement learning neural turing machines-revised. arXiv preprint arXiv:1505.00521 (2015)
Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Greco, C., Suglia, A., Basile, P., Semeraro, G. (2017). Converse-Et-Impera: Exploiting Deep Learning and Hierarchical Reinforcement Learning for Conversational Recommender Systems. In: Esposito, F., Basili, R., Ferilli, S., Lisi, F. (eds) AI*IA 2017 Advances in Artificial Intelligence. AI*IA 2017. Lecture Notes in Computer Science(), vol 10640. Springer, Cham. https://doi.org/10.1007/978-3-319-70169-1_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-70169-1_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70168-4
Online ISBN: 978-3-319-70169-1
eBook Packages: Computer ScienceComputer Science (R0)