Abstract
Utilizing Global Positioning System (GPS) technology, it is possible to find and recommend restaurants for users operating mobile devices. For recommending restaurants, Personal Digital Assistants or cellular phones only consider the location of restaurants. However, a user’s background and environment information is assumed to be directly related to recommendation quality. In this paper, therefore, a recommender system using context information and a decision tree model for efficient recommendation is presented. This system considers location context, personal context, environment context, and user preference. Restaurant lists are obtained from location context, personal context, and environment context using the decision tree model. In addition, a weight value is used for reflecting user preferences. Finally, the system recommends appropriate restaurants to the mobile user. For this experiment, performance was verified using measurements such as k-fold cross-validation and Mean Absolute Error. As a result, the proposed system obtained an improvement in recommendation performance.
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References
Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., McNee, S.M., Konstan, J.A., Riedl, J.: Getting to Know you: Learning New User Preferences in Recommender Systems. In: Proc. of the 7th Int. Conf. on Intelligent User Interfaces, pp. 127–134 (2002)
Cheverst, K., Davies, N., Mitchell, K., Friday, A., Efstratiou, C.: Developing a Context-aware Electronic Tourist Guide: Some Issues and Experiences. In: Proc. of the CHI 2000 Conf. on Human Factors in Computing System, pp. 17–24. ACM, New York (2000)
Schopp, B., Ropnack, A., Markus, G.: The Need for Topological Time and Location in Mobile E-Business Applications. In: Proc. of the 9th Euromicro Workshop on Parallel and Distributed Processing (2001)
Tang, H., Soo, V.: A Personalized Restaurant Recommender Agent for Mobile E-Service. In: Proc. of the Conf. on E-Technology, E-Commerce and E-Service, March 2004, pp. 259–262. IEEE, Los Alamitos (2004)
Park, K., Lee, H.: Study on Family Restaurant Recommendation for Customers based on Benefit Sought and Demographical Variables. In: Proc. on The Korea Academic Society of Tourism and Leisure (2003)
Van Diggelen, F.: Indoor GPS Theory and Implementation. In: IEEE Position, Location & Navigation Symposium (2002)
Pazzani, M.J.: A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligent Review (1999)
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sarti, M.: Combining Contents-Based and Collaborative Filters in an Online Newspaper. In: ACM SIGIR Workshop on Recommender Systems, Berkeley, CA (1999)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based Collaborative Filtering Recommendation Algorithms. In: Proc. of the Tenth International World Wide Web Conference on World Wide Web (2001)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)
Good, N., Schafer, B., Konstan, J., Borchers, A., Sarwar, B., Herlocker, J., Riedle, J.: Combining Collaborative Filtering with Personal Agents for Better Recommendation. In: Proc. of the AAAI conference, pp. 439–446 (1999)
Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating “Word of Mouth”. In: Proc. of the SIGCHI Conf. on Human Factors in Computing Systems (1995)
Dey, A.K., Abowd, G.D.: Towards a better understanding of Context and Context-Awareness. GVU Technical Report GITGVU-99-22, College of Computing, Georgia Institute of Technolgy 2, 2–14 (1999)
Chen, G., Kotz, D.: A Survey of Context-Aware Mobile Computing Research. Dartmouth Computer Science Technical Report TR2000-381 (2000)
Singh, S., Vajirkar, P., Lee, Y.: Context-Based Data Mining Using Ontologies. In: Song, I.-Y., Liddle, S.W., Ling, T.-W., Scheuermann, P. (eds.) ER 2003, vol. 2813, pp. 405–418. Springer, Heidelberg (2003)
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Lee, BH., Kim, HN., Jung, JG., Jo, GS. (2006). Location-Based Service with Context Data for a Restaurant Recommendation. In: Bressan, S., Küng, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2006. Lecture Notes in Computer Science, vol 4080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11827405_42
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DOI: https://doi.org/10.1007/11827405_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37871-6
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