Abstract
When we discuss about recommendation especially in Location-Based Services (LBS), we need to reveal whether users really want recommendations or not in fact while they are strolling in town, prior to evaluate each recommendation model.
In this paper, a Location-Based Service, called nicotoco, is shown. nicotoco is an iPhone-based LBS in Futako-tamagawa area, Tokyo, Japan and provides information about stores and events to users. In the experiment using nicotoco, recommendations may be preferred more than rankings which was made from access counts.
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Aihara, K., Koshiba, H., Takeda, H.: Behavioral cost-based recommendation model for wanderers in town. In: Jacko, J.A. (ed.) Human-Computer Interaction, Part III, HCII 2011. LNCS, vol. 6763, pp. 271–279. Springer, Heidelberg (2011)
Braak, P.T., Abdullah, N., Xu, Y.: Improving the performance of collaborative filtering recommender systems through user profile clustering. In: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, vol. 3, pp. 147–150 (2009)
Ducheneaut, N., Partridge, K., Huang, Q., Price, B., Roberts, M., Chi, E.H., Bellotti, V., Begole, B.: Collaborative filtering is not enough? experiments with a mixed-model recommender for leisure activities. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 295–306. Springer, Heidelberg (2009)
Green, L., Fry, A.F., Myerson, J.: Discounting of delayed rewards: A life-span comparison. Psychological Science 5(1), 33–36 (1994)
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Advances in Artificial Intelligence 2009, 1–19 (2009)
Tversky, A., Kahneman, D.: the framing of decisions and the psychology of choice. Science 211(4481), 453–458 (1981)
Zheng, V.W., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence, pp. 236–241 (2010)
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Aihara, K. (2013). Do Strollers in Town Needs Recommendation?: On Preferences of Recommender in Location-Based Services. In: Streitz, N., Stephanidis, C. (eds) Distributed, Ambient, and Pervasive Interactions. DAPI 2013. Lecture Notes in Computer Science, vol 8028. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39351-8_30
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DOI: https://doi.org/10.1007/978-3-642-39351-8_30
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