THE EVALUATION OF A RECOMMENDATION SYSTEM FOR TOURIST DESTINATION DECISION MAKING

The present paper will outline the creation and evaluation of an intelligent tourist recommendation system named DieToRecs. Recommender systems became a significant tool in the field of tourism; they offer users a convenient opportunity to find a travel bundle or a single travel item such as accommodation. Existing tourist recommendation systems have some shortcomings as they allow no or only very limited flexibility when taking constraints or preferences into account. Therefore, the aim to design a novel type of recommendation system including features such as Collaborative Filtering and Case Based Reasoning (CBR) is pursued by the DieToRecs project team. Before the final system will be implemented, two prototypes are created. Experts and user groups evaluate each of the prototypes to assure an improvement and to enable the best final version possible. The evaluation considers aspects such as design and layout, functionality or ease of use. An aim is to analyze the results of the assessments. The ultimate goal of the thesis is to realize a meta-evaluation meaning to assess which methods are the most effective and useful ones regarding the analysis of tourist recommendation systems.

[1]  Andreas H. Zins,et al.  Vacation Trip Decision Styles as Basis for an Automated Recommendation System: Lessons from Observational Studies , 2002, ENTER.

[2]  Andreas H. Zins,et al.  Adapting to Cognitive Styles to improve the Usability of Travel Recommendation Systems , 2003, ENTER.

[3]  J. B. Brooke,et al.  SUS: A 'Quick and Dirty' Usability Scale , 1996 .

[4]  Albert L. Lederer,et al.  The technology acceptance model and the World Wide Web , 2000, Decis. Support Syst..

[5]  Francesco Ricci,et al.  Intelligent Query Management for Travel Products Selection , 2002, Information and Communication Technologies in Tourism.

[6]  Arno Scharl,et al.  Success Factors of European Hotel Web Sites , 2002, ENTER.

[7]  Ulrike Gretzel,et al.  Behavioral Foundations for Human-Centric Travel Decision-Aid Systems , 2002, ENTER.

[8]  Donna L. Hoffman,et al.  Measuring the Customer Experience in Online Environments: A Structural Modeling Approach , 2000 .

[9]  Miyoung Jeong,et al.  Adaptation of an information quality framework to measure customers’ behavioral intentions to use lodging Web sites , 2001 .

[10]  James R. Lewis,et al.  IBM computer usability satisfaction questionnaires: Psychometric evaluation and instructions for use , 1995, Int. J. Hum. Comput. Interact..

[11]  D. Hoffman,et al.  Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations , 1996 .

[12]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[13]  Vincent S. Lai,et al.  Prediction of Internet and World Wide Web usage at work: a test of an extended Triandis model , 2000, Decis. Support Syst..

[14]  A. Parasuraman,et al.  SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. , 1988 .

[15]  Marty Landrigan.  Improving Your Measurement of Customer Satisfaction , 1999 .

[16]  Bora Arslan,et al.  Case Based Session Modeling and Personalization in a Travel Advisory System , 2002 .

[17]  Francesco Ricci,et al.  ITR: A Case-Based Travel Advisory System , 2002, ECCBR.

[18]  Gitte Lindgaard.  Usability testing and system evaluation - a guide for designing useful computer systems , 1994, Chapman and Hall computing series.

[19]  Richard Butler,et al.  Perceptions of Marketing Managers of the Effectiveness of the Internet in Tourism and Hospitality , 2000, J. Inf. Technol. Tour..

[20]  Hildegard Rumetshofer,et al.  Extended Decision Making in Tourism Information Systems , 2002, EC-Web.