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Analyzing Topic Models: A Tourism Recommender System Perspective

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Advanced Information Networking and Applications (AINA 2024)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 200))

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Abstract

Topic Modeling is a well-known text-mining strategy that detects potential underlying topics for documents. It plays a pivotal role in recommender systems for processing proliferated user-generated content (UGC) for personalized recommendations. Its application presents unique challenges in tourism sector due to the diversity, dynamicity, and multimodality of tourism data. This study presents a comprehensive analysis of selected promising topic models specifically in context of tourism recommender systems. The study conducts experimental evaluation of models’ performance on five datasets, and highlights their advantages and unique characteristics based on multiple evaluation parameters. Results reveal no best approach in general, rather optimality of models depend on data characteristics, as thoroughly discussed in this paper. It further discusses open issues for the tourism context-related application of topic models, and future research directions.

This research is supported by Amarena Company srl.

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Authors and Affiliations

  1. Department of Computer, Control and Management Engineering (DIAG), Sapienza University of Rome, 00185, Rome, Italy

    Maryam Kamal, Gianfranco Romani, Aris Anagnostopoulos & Ioannis Chatzigiannakis

  2. Amarena Company Srl, Via Casilina 3T, 00182, Rome, Italy

    Giuseppe Ricciuti

Authors
  1. Maryam Kamal
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  2. Gianfranco Romani
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  3. Giuseppe Ricciuti
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  4. Aris Anagnostopoulos
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  5. Ioannis Chatzigiannakis
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Corresponding author

Correspondence to Ioannis Chatzigiannakis .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Kamal, M., Romani, G., Ricciuti, G., Anagnostopoulos, A., Chatzigiannakis, I. (2024). Analyzing Topic Models: A Tourism Recommender System Perspective. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 200. Springer, Cham. https://doi.org/10.1007/978-3-031-57853-3_21

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