domain offers considerably more complicated scenarios than matching travelers with the presumably best items. Planning a vacation usually involves searching for interconnected and dependent product bundles, such as means of transportation, accommodations, attractions, and activities, with limited availabilities and contextual aspects (e.g., spatio-temporal context, social context, activity sequence, and environment) with a major impact. In addition, travel related products are emotionally “loaded” and thus largely experiential in nature; therefore, decision taking is often not solely based on rational or objective criteria. Therefore, information provisioning at the right time about destinations, accommodations and various further services and possible activities is challenging. Additionally, and in contrast to many other recommendation domains, information providers are usually small and medium sized enterprises (SMEs) that many times do not possess the capacity to implement basic recommender systems. Moreover, there is no single, standard format to house information which might be included in these systems. Last, much of the tourism experience is co-produced, i.e., it occurs during the consumption of the product and interaction with the provider.
recommender systems within the tourism domain is extremely challenging.
The rapid development of information and communication technologies (ICT) in general and the web in particular has transformed the tourism domain whereby most travelers rely little on travel agents or agencies. Indeed, recent studies indicate that travelers now actively search for information using ICT in order to compose their vacation packages according to their specific emotionally driven preferences. Additionally when on-site, they search for freely available information about the site itself rather than renting a visitor guide that may be available, but considered to be expensive and sometimes outdated. However, like in many other cases, the blessing of the web comes with a curse; the curse of information overload. As such, recommender systems have been suggested as a practical tool for overcoming this information overload. However, because the tourism domain is extremely complex, those designing tourism-focused recommender systems face huge challenges.
This workshop brings together researchers and practitioners from different fields (e.g., tourism, recommender sys tems, user modeling, user interaction, mobile, ubiquitous and ambient technologies, artificial intelligence and web in formation systems) working in the tourism recommendation domain. The workshop aims to provide a forum for these people to discuss novel ideas for addressing the specific challenges for recommender systems in tourism with the goal to advance the current state-of-the-art in this field. Another goal of the workshop is to identify practical applications of these technologies within tourism settings from the point of view of individual users and user groups, service providers, as well as from additional stakeholders (e.g., destination management organizations). Finally, RecTour 2018 aims to continue the community building processes and discussions started at previous RecTour Workshops, i.e., at
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• Julia Neidhardt, TU Wien, Austria • Wolfgang Wörndl, TU München, Germany • Tsvi Kuflik , The University of Haifa, Israel • Markus Zanker, Free University of Bozen/Bolzano, Italy Program Committee • Catalin-Mihai Barbu, University of Duisburg-Essen, Germany • Derek Bridge, University College Cork, Ireland • Amra Delic, TU Wien, Austria • Damianos Gavalas, University of the Aegean, Greece • Ulrike Gretzel, University of Southern California, USA • Wilfried Grossmann, University of Vienna, Austria • Daniel Herzog, TU München, Germany • Dietmar Jannach, TU Dortmund, Germany • Jan Krasnodebski, Expedia, Switzerland • Antonio Moreno, Universitat Rovira i Virgili, Spain • Francesco Ricci, University of Bozen/Bolzano, Italy • Hannes Werthner, TU Wien, Austria • Zheng Xiang, Virginia Tech, USA • David Zibriczky, trivago, Germany
11:00 - 12:30 Session 2: Hotel Recommendation and Trip Planning • Marie Al-Ghossein, Talel Abdessalem and Anthony Barré: Cross-Domain Recommendation in the Hotel Sector. • Catalin-Mihai Barbu and Jürgen Ziegler: Designing Interactive Visualizations of Personalized Review Data for a Hotel Recommender System. • Daniel Herzog, Nikolaos Promponas-Kefalas and Wolfgang Wörndl: Integrating Public Displays into Tourist Trip Recommender Systems. • Koji Kawamata and Kenta Oku. Roadscape-based Route Recommender System using Coarse-to-fine Route Search. 12:30 - 14:00 Lunch Break 14:00 - 15:30 Session 3: Industry Perspective • Keynote User Experience and Data: Bridging the Gap by Themis Mavridis (Booking.com, Netherlands) • Alejandro Mottini, Alix Lheritier, Rodrigo Acuna-Agost and Maria A. Zuluaga: Understanding Customer Choices to Improve Recommendations in the Air Travel Industry. 15:30 - 16:00 Coffee Break 16:00 - 17:30 Session 4: Crowdsourcing and Evaluation • Öykü Kapcak, Simone Spagnoli, Vincent Robbemond, Soumitri Vadali, Shabnam Najafian and Nava Tin tarev: TourExplain: A Crowdsourcing Pipeline for Generating Explanations for Groups of Tourists. • Linus W. Dietz and Achim Weimert: Recommending Crowdsourced Trips on wOndary. • Pablo Sánchez and Alejandro Bellogin: Challenges on Evaluating Venue Recommendation Approaches: Position Paper. • Workshop closing Keynote Recommender Systems in Tourism:
A Case for Interactive Approaches by Dietmar Jannach, Alpen-Adria-Universität Klagenfurt, Austria
texts, from the selection of a destination, over pre-trip itinerary planning, to point-of-interest recommendation during the trip. In many of these application scenarios, building a recommender system solely on long-term interest profiles is not possible. Instead, interactive approaches are re quired in which users have the opportunity to interactively state and eventually revise their needs and preferences, and where the system is also able to explain its suggestions.
Keynote User Experience and Data: Bridging the Gap by Themis Mavridis, Booking.com, Netherlands
verse and rich inventory. It aims to provide optimal shopping experience for our guests even with minimal user interaction. In this talk, I will discuss about the various unique and complex business cases, and the challenges that we face while we strive to bridge the gap between user experience and data. Finally, I will describe how we successfully utilize Machine Learning validated through rigorous Randomized Controlled Experiments in every step of the user journey.