ABSTRACT
This paper presents a field study of a framework for personalized mobile recommendations in the tourism domain, of sight-seeing Points of Interest (POI). We evaluate the effectiveness, satisfaction and divergence from popularity of a knowledge-based personalization strategy comparing it to recommending most popular sites. We found that participants visited more of the recommended POIs for lists with popular but non-personalized recommendations. In contrast, the personalized recommendations led participants to visit more POIs overall and visit places "off the beaten track". The level of satisfaction between the two conditions was comparable and high, suggesting that our participants were just as happy with the rarer, "off the beaten track" recommendations and their overall experience. We conclude that personalized recommendations set tourists into a discovery mode with an increased chance for serendipitous findings, in particular for returning tourists.
- Android location manager. Last retrieved Dec. 2009. http://developer.android.com/reference/android/.Google Scholar
- Api call to getpoi. Last retrieved Dec. 2009. Anonymized.Google Scholar
- Geocoder. Last retrieved Dec. 2009. http://code.google.com/apis/maps/ documentation/services.html.Google Scholar
- Personalization questionnaire. http://78.46.87.99/tourist/.Google Scholar
- Post-questionnaire. http://78.46.87.99/tourist/postQnaire.doc.Google Scholar
- Pre-questionnaire. http://78.46.87.99/tourist/preQnaire.doc.Google Scholar
- Reality mining at MIT. Last retrieved Dec. 2009. http://reality.media.mit.edu/.Google Scholar
- Ski-europe. Last retrieved Dec. 2009. http://www.ski-europe.com.Google Scholar
- Travel ontology. Last retrieved Dec. 2009. http://www.schemaweb.info/schema/ SchemaDetails.aspx?id=236.Google Scholar
- Tripsay. Last retrieved Dec. 2009. http://www.tripsay.com.Google Scholar
- W3c geolocation api. Last retrieved Dec. 2009. http://dev.w3.org/geo/api/spec-source.html.Google Scholar
- Wikipedia. Last retrieved Dec. 2009. http://en.wikipedia.org/.Google Scholar
- G. D. Abowd, C. G. Atkeson, J. Hong, S. Long, R. Kooper, and M. Pinkerton. Cyberguide: A mobile context guide. In ACM Wireless Networks, pages 421--433, 1997. Google ScholarDigital Library
- C. Anderson. The Long Tail: Why the Future of Business is Selling Less of More. Hyperion, 2006. Google ScholarDigital Library
- L. Ardissono, A. Goy, G. Petrone, M. Segnan, and P. Torasso. Intrigue: Personalized recommendation of tourist attractions for desktop and handset devices. In Applied Artifical Intelligence, 2003.Google ScholarCross Ref
- M. Bilgic and R. J. Mooney. Explaining recommendations: Satisfaction vs. promotion. In Proceedings of the Workshop Beyond Personalization, in conjunction with the International Conference on Intelligent User Interfaces, pages 13--18, 2005.Google Scholar
- F. Bohnert and I. Zukerman. Non-intrusive personalisation of the museum experience. In UMAP, pages 197--209, 2009. Google ScholarDigital Library
- M. Braun and A. Scherp. Collaborative poi. Last retrieved Dec. 2009. http://isweb.uni-koblenz.de/Research/systeme/csxPOI.Google Scholar
- K. Cheverest, N. Davies, K. Mitchel, A. Friday, and C. Efstratiou. Developing a context-aware electronic tourist guide: Some issues and experiences. In CHI Letters, 2000. Google ScholarDigital Library
- K. Church, J. Neumann, M. Cherubin, and N. Oliver. Socialsearchbrowser: A novel mobile search and information discovery tool. In In Proceedings of the International Conference on Intelligent User Interfaces (IUI), 2010. Google ScholarDigital Library
- J. Delgado and R. Davidson. Knowledge bases and user profiling in travel and hospitality recommender systems. In Proceedings of the ENTER 2002 Conference,, pages pp. 1--16, Innsbruck, Austria, January 22-25 2002. Springer Verlag.Google ScholarCross Ref
- A. Felfernig, S. Gordea, D. Jannach, E. Teppan, and M. Zanker. A short survey of recommendation technologies in travel and tourism. ÖGAI Journal, 25:17==22, 2007.Google Scholar
- D. Jannach and K. Hegelich. A case study on the effectiveness of recommendations in the mobile internet. In Conference on Recommender systems, pages 205--208, 2009. Google ScholarDigital Library
- J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl. Grouplens: Applying collaborative filtering to usenet news. Communications of the ACM, 40(3):77--87, Mar. 1997. Google ScholarDigital Library
- J. Masthoff. Group modeling: Selecting a sequence of television items to suit a group of viewers. User Modeling and User Adapted Interaction, 14:37--85, 2004. Google ScholarDigital Library
- D. McSherry. Explanation in recommender systems. Artificial Intelligence Review, 24(2):179--197, 2005. Google ScholarDigital Library
- M. Perrero, F. Antonelli, and M. Geymonat. Recommendation on tv: What do users want? a user study. In Recommender-based Industrial Applications Workshop in association with the conference on Recommender Systems, 2009.Google Scholar
- D. Rowland, M. F. L. Oppermann, J. Marshall, A. C. andBoriana Koleva, S. Benford, and C. Perez. Ubikequitous computing: Designing interactive experiences for cyclists. In MobileHCI, 2009. Google ScholarDigital Library
- M. van Setten, S. Pokraev, and J. Koolwaaji. Context-aware recommendations in the mobile tourist application compass. In Adaptive Hypermedia, pages 235--244, 2004.Google ScholarCross Ref
- R. van Zwol and B. Sigurbj¨ornsson. Faceted exploration of image search results. In WWW, 2010. Google ScholarDigital Library
- Waszkiewicz, Cunnigham, and Byrne. Case-based personal travel agent. In International Conference on User Modeling, 1999.Google Scholar
- C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. In WWW'05, 2005. Google ScholarDigital Library
Index Terms
- Off the beaten track: a mobile field study exploring the long tail of tourist recommendations
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