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Session-based Hotel Recommendations Dataset – As part of the ACM Recommender System Challenge 2019

Adamczak, Jens; Deldjoo, Yashar; Moghaddam, Farshad Bakhshandegan 1; Knees, Peter; Leyson, Gerard-Paul; Monreal, Philipp
1 Karlsruher Institut für Technologie (KIT)

Abstract:

In 2019, the Recommender Systems Challenge [17] dealt for the first time with a real-world task from the area of e-tourism, namely the recommendation of hotels in booking sessions. In this context, we present the release of a new dataset that we believe is vitally important for recommendation systems research in the area of hotel search, from both academic and industry perspectives. In this article, we describe the qualitative characteristics of the dataset and present the comparison of several baseline algorithms trained on the data.


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Originalveröffentlichung
DOI: 10.1145/3412379
Scopus
Zitationen: 16
Web of Science
Zitationen: 14
Dimensions
Zitationen: 18
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 02.2021
Sprache Englisch
Identifikator ISSN: 2157-6904, 2157-6912
KITopen-ID: 1000133071
Erschienen in ACM Transactions on Intelligent Systems and Technology
Verlag Association for Computing Machinery (ACM)
Band 12
Heft 1
Seiten Art. Nr.: 1
Externe Relationen Abstract/Volltext
Schlagwörter Dataset, session-based recommender systems, context-aware recom-mender systems, tourism, hotel recommendation
Nachgewiesen in Dimensions
Web of Science
Scopus
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