Abstract
When computer programs participate in conversations, they can learn things about the people they are conversing with. A conversational system that helps a user select a flight may notice that a person prefers a particular seating arrangement or departure airport. In this paper we discuss a system which uses the information state accumulated during a person-machine conversation and a case-based analysis to derive preferences for the person participating in that conversation. We describe the implementation of this system based on a MapReduce framework that allows for near real-time generation of a user’s preferences regardless of the total case memory size. We also show some preliminary performance results from scaling tests.
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References
Bobrow, D.G., Kaplan, R.M., Kay, M., Norman, D.A., Thompson, H., Winograd, T.: GUS, a frame-driven dialog system. Artificial Intelligence 8(2), 155–173 (1977)
Levin, E., Narayanan, S., Pieraccini, R., Biatov, K., Bocchieri, E., Di Fabbrizio, G., Walker, M.: The AT&T-DARPA Communicator mixed-initiative spoken dialog system. In: Proc. of ICSLP, vol. 2, pp. 122–125 (October 2000)
Bohus, D., Rudnicky, A.I.: The RavenClaw dialog management framework: Architecture and systems. Computer Speech & Language 23(3), 332–361 (2009)
Saaya, Z., Smyth, B., Coyle, M., Briggs, P.: Recommending case bases: applications in social web search. Case-Based Reasoning Research and Development, 274–288 (2011)
Bridge, D., Göker, M.H., McGinty, L., Smyth, B.: Case-based recommender systems. The Knowledge Engineering Review 20(03), 315–320 (2005)
Sugiyama, K., Hatano, K., Yoshikawa, M.: Adaptive web search based on user profile constructed without any effort from users. In: Proceedings of the 13th International Conference on World Wide Web, pp. 675–684. ACM (May 2004)
Schiaffino, S.N., Amandi, A.: User profiling with case-based reasoning and bayesian networks. In: IBERAMIA-SBIA 2000 Open Discussion Track, pp. 12–21 (2000)
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Communications of the ACM 51(1), 107–113 (2008)
Berube, D.: Encode video with MongoDB work queues, http://www.ibm.com/developerworks/library/os-mongodb-work-queues
Bonnet, L., Laurent, A., Sala, M., Laurent, B., Sicard, N.: Reduce, You Say: What NoSQL Can Do for Data Aggregation and BI in Large Repositories. In: 2011 22nd International Workshop on Database and Expert Systems Applications (DEXA), pp. 483–488. IEEE (August 2011)
Horowitz, E.: Schema Design at Scale. Presentation, MongoSV (2011), http://www.10gen.com/presentations/mongosv-2011/schema-design-at-scale
MongoDB 2.4 Release Notes, http://docs.mongodb.org/manual/release-notes/2.4/#default-javascript-engine-switched-to-v8-from-spidermonkey
Horowitz, E.: The Secret Sauce of Sharding. Presentation, MongoSF (2011), http://www.10gen.com/presentations/mongosf2011/sharding
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Beaver, I., Dumoulin, J. (2013). Applying MapReduce to Learning User Preferences in Near Real-Time. In: Delany, S.J., Ontañón, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2013. Lecture Notes in Computer Science(), vol 7969. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39056-2_2
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DOI: https://doi.org/10.1007/978-3-642-39056-2_2
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