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https://ceur-ws.org/Vol-3219/preface.pdf
RecTour 2022
Workshop on Recommenders in Tourism
Seattle, WA, USA and Virtual, September 22nd, 2022
Proceedings
Edited by
Julia Neidhardt, Wolfgang Wörndl, Tsvi Kuflik
Dmitri Goldenberg & Markus Zanker
Co-located with the 16th ACM Conference
on Recommender Systems (RecSys 2022)
© 2012 Wolfgang Wörndl
© 2012 Julia Neidhardt
Copyright and Bibliographical Information
Copyright © 2022 for the individual papers by the papers’ authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0). This volume is published and copyrighted by its editors. The copyright for
papers appearing in these proceedings belongs to the papers’ authors.
This volume is published by Julia Neidhardt, Wolfgang Wörndl, Tsvi Kuflik, Dmitri Goldenberg & Markus Zanker.
Proceedings of the Workshop on Recommenders in Tourism (RecTour 2022), held in conjunction with the 16th
ACM Conference on Recommender Systems (RecSys 2022), September 18th – 23rd, 2022, Seattle, WA, USA and
virtual, https://recsys.acm.org/recsys22/.
Julia Neidhardt, Wolfgang Wörndl, Tsvi Kuflik, Dmitri Goldenberg & Markus Zanker (editors).
Further information about the workshop can be found at: https://web.ec.tuwien.ac.at/rectour22/
Preface
This volume contains the contributions of the Workshop on Recommenders in Tourism (RecTour), organized in
conjunction with the 16th ACM Conference on Recommender System (RecSys 2022) in Seattle, WA, USA.
RecTour 2022 focuses on a variety of challenges specific to recommender systems in the tourism domain. This
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) having a major impact. In addition,
travel related products can be considered as emotionally loaded and are 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. Therefore, the context of the recommendation is extremely important. Thus, given
this diversity, building effective 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 tour- ism 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, those designing tourism-focused recommender
systems face huge challenges as the tourism domain is extremely complex.
This workshop brings together researchers and practitioners from different fields (e.g., tourism, recommender
systems, user modeling, mobile technologies, artificial intelligence and web information systems) working in the
tourism recommendation domain. The workshop aims to provide a forum for these people to discuss novel ideas 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).
RecTour 2022 aims to continue the community building processes and discussions started at previous RecTour
Workshops. Topics at RecTour 2022 particularly deal with recommending destinations, itinerary recommendation
online hotel search and novel perspectives on personalization.
September 2022 Julia Neidhardt, Wolfgang Wörndl, Tsvi Kuflik Dmitri Goldenberg & Markus Zanker
i
Workshop Committees
Organizers
• Julia Neidhardt, Christian Doppler Laboratory for Recommender Systems, TU Wien, Austria
• Wolfgang Wörndl, Department of Informatics, TU München, Germany
• Tsvi Kuflik, Information Systems Department, The University of Haifa, Israel
• Dmitri Goldenberg, Booking.com, Tel Aviv, Israel
• Markus Zanker, Free University of Bozen/Bolzano, Italy and University of Klagenfurt, Austria
Program Committee
• Alejandro Bellogin, Universidad Autonoma de Madrid, Spain
• Derek Bridge, University College Cork, Ireland
• Ludovik Coba, Expedia Group, UK
• Amra Delić, University of Sarajevo, Bosnia and Herzegovina
• Linus W. Dietz, TU München, Germany
• Damianos Gavalas, University of the Aegean, Greece
• Ulrike Gretzel, University of Southern California, USA
• Dietmar Jannach, AAU Klagenfurt, Austria
• Jan Krasnodebski, Expedia Group, Switzerland
• David Massimo, Free University of Bozen/Bolzano, Italy
• Francesco Ricci, Free University of Bozen/Bolzano, Italy
• Gabriele Sottocornola, Free University of Bozen/Bolzano, Italy
Acknowledgement
ii
Workshop Program
9 AM - 12:30 PM (PDT) On-Site Session
• Workshop Opening
• Keynote by Robin Burke
• Emanuele Cavenaghi, Lorenzo Camaione, Paolo Minasi, Gabriele Sottocornola, Fabio Stella and
Markus Zanker: An Online Experiment of a Price-Based Re-Rank Algorithm
• Short Break
• Abhishek Agarwal and Linus W. Dietz: Recommending the Duration of Stay in Personalized Travel
Recommender Systems
• Evripides Christodoulou, Andreas Gregoriades, Herodotos Herodotou and Maria Pampaka:
Combination of User and Venue Personality with Topic Modelling in Restaurant Recommender Systems
• Chana Ross, Tomer Ovadia, Jake Mooney, Amit Meitin, Eytan Kabilou, Mush Kabalo and Dmitri
Goldenberg: Democratizing Travel Personalization via Central Recommendation Platform
• Wrap-Up and Discussion
2:30 PM - 4 PM (PDT) Virtual Session
• Costas Panagiotakis, Evangelia Daskalaki, Harris Papadakis and Paraskevi Fragopoulou: The Tourist
Trip Design Problem with POI Categories via an Expectation-Maximization Based Method
• Errikos Streviniotis and Georgios Chalkiadakis: Multiwinner Election Mechanisms for Diverse
Personalized Bayesian Recommendations for the Tourism Domain
• Keisuke Otaki and Yukino Baba: Maximal Likelihood Itinerary Planning with User Interaction Data
• Final Wrap-Up and Workshop Closing
iii
Tourism Recommendation: Re-Thinking the User
Keynote by Robin Burke (University of Colorado, Boulder, USA)
Abstract
From its inception, research in recommender systems has focused almost exclusively
on the individual end user or users to whom recommendations are delivered. The
limits of this approach have become apparent as the field moves towards a greater
recognition of the perspectives of multiple stakeholders. The incorporation of the
multiple viewpoints in recommender systems presents a challenge to our
understanding of the “user” of a recommender system and to accepted notions of
how recommender systems should be designed, operated and evaluated. In tourism,
we see a diversity of non-end-user stakeholders from individual entrepreneurs and
large corporations to non-profits and governmental entities, and a range of types of
concerns from profit to fairness to ecological impact. What would recommender
systems research look like if we considered these stakeholders to be first-class users of our systems and not just
bystanders? In this talk, we’ll look at the challenges and benefits of such an approach for the tourism domain.
About the speaker
Robin Burke is Professor and Chair of the Department of Information Science at the University of Colorado, Boulder.
He conducts research in personalized recommender systems, a field he helped found and develop. His most recent
projects explore fairness, accountability and transparency in recommendation through the integration of objectives
from diverse stakeholders. He joined the Department of Information Science in 2019 from the School of Computing at
DePaul University. Dr Burke obtained his PhD in Computer Science from Northwestern University in 1993 and a BS
in Computer Science from Harvey Mudd College in 1986. Professor Burke is the author of more than 100 peer-
reviewed articles in various areas of artificial intelligence including recommender systems, machine learning and
information retrieval. His work has received support from the National Science Foundation, the National Endowment
for the Humanities, the Fulbright Commission and the MacArthur Foundation, among others.
iv
Table of Contents
Long Papers
• Abhishek Agarwal and Linus W. Dietz: Recommending the Duration of Stay in Personalized Travel
Recommender Systems 1 – 20
• Evripides Christodoulou, Andreas Gregoriades, Herodotos Herodotou and Maria Pampaka:
Combination of User and Venue Personality with Topic Modelling in Restaurant Recommender Systems 21 –
36
• Keisuke Otaki and Yukino Baba: Maximal Likelihood Itinerary Planning with User Interaction Data 37 –
46
• Costas Panagiotakis, Evangelia Daskalaki, Harris Papadakis and Paraskevi Fragopoulou: The Tourist
Trip Design Problem with POI Categories via an Expectation-Maximization Based Method 47 – 64
• Errikos Streviniotis and Georgios Chalkiadakis: Multiwinner Election Mechanisms for Diverse
Personalized Bayesian Recommendations for the Tourism Domain 65 – 82
Short Papers
• Emanuele Cavenaghi, Lorenzo Camaione, Paolo Minasi, Gabriele Sottocornola, Fabio Stella and
Markus Zanker: An Online Experiment of a Price-Based Re-Rank Algorithm 83 – 91
• Chana Ross, Tomer Ovadia, Jake Mooney, Amit Meitin, Eytan Kabilou, Mush Kabalo and Dmitri
Goldenberg: Democratizing Travel Personalization via Central Recommendation Platform 92 – 97
v