A two-step, user-centered approach to personalized tourist recommendations

Geo-localized, mobile applications can simplify a tourist visit, making the relevant Point of Interests more easily and promptly discernible to users. At the same time, such solutions must avoid creating unfitting or rigid user profiles that impoverish the users' options instead of refining them. Currently, user profiles in recommender systems rely on dimensions whose relevance to the user is more often presumed than empirically defined. To avoid this drawback, we build our recommendation system in a two-step process, where profile parameters are evaluated preliminarily and separately from the recommendations themselves. We describe this two-step evaluation process including an initial survey (N = 206), and a subsequent controlled study (N = 24). We conclude by emphasizing the benefit and generalizability of the approach

[1]  Francesco Ricci,et al.  Mobile Recommender Systems , 2010, J. Inf. Technol. Tour..

[2]  Nava Tintarev,et al.  Evaluating the effectiveness of explanations for recommender systems , 2012, User Modeling and User-Adapted Interaction.

[3]  Sean M. McNee,et al.  Being accurate is not enough: how accuracy metrics have hurt recommender systems , 2006, CHI Extended Abstracts.

[4]  Guang-Zhong Yang,et al.  Body sensor networks , 2006 .

[5]  Birgitta König-Ries,et al.  An approach to controlling user models and personalization effects in recommender systems , 2013, IUI '13.

[6]  Charalampos Konstantopoulos,et al.  Mobile recommender systems in tourism , 2014, J. Netw. Comput. Appl..

[7]  Zhongfu Wu,et al.  Personalisation in web computing and informatics: Theories, techniques, applications, and future research , 2010, Inf. Syst. Frontiers.

[8]  Rong Hu,et al.  A Study on User Perception of Personality-Based Recommender Systems , 2010, UMAP.

[9]  A. Felfernig,et al.  A Short Survey of Recommendation Technologies in Travel and Tourism , 2006 .

[10]  Katherine Gallagher,et al.  Using viewing time to infer user preference in recommender systems , 2004 .

[11]  Dimitrios Buhalis,et al.  A Typology of Technology‐Enhanced Tourism Experiences , 2014 .

[12]  Martin Hepp,et al.  Effects of the Placement of Diverse Items in Recommendation Lists , 2012, ICEIS.

[13]  Stathes Hadjiefthymiades,et al.  Facing the cold start problem in recommender systems , 2014, Expert Syst. Appl..

[14]  Dong Chen,et al.  Identifying places of interest for tourists using knowledge discovery techniques , 2014, 2014 International Conference on Industrial Automation, Information and Communications Technology.

[15]  Franca Garzotto,et al.  Comparative evaluation of recommender system quality , 2011, CHI Extended Abstracts.

[16]  Natan Uriely The tourist experience: Conceptual Developments , 2005 .

[17]  Franca Garzotto,et al.  User profiling vs. accuracy in recommender system user experience , 2012, AVI.

[18]  Alexander Felfernig,et al.  Recommender Systems: An Overview , 2011, AI Mag..

[19]  Gerhard Friedrich,et al.  Recommender Systems - An Introduction , 2010 .