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Unlocking the Potential of Content-Based Restaurant Recommender Systems

In: Information and Communication Technologies in Tourism 2024

Author

Listed:
  • Dante Godolja

    (TU Wien)

  • Thomas Elmar Kolb

    (TU Wien)

  • Julia Neidhardt

    (TU Wien)

Abstract

Content-based restaurant recommender systems use features such as cuisine type, price range, and location to suggest dining options to users. Current research explores ways to improve their effectiveness. In this work, we explore different ideas on how to build a recommender system. We explore TF-IDF as a baseline and the state-of-the-art model SBERT. These ideas are tested on a real-world data-set of a digital restaurant guide. Evaluation involves both qualitative assessment by a domain expert and quantitative analysis. The results show that, with proper preprocessing, TF-IDF can achieve similar scores to SBERT and, depending on the scenario, even better results. However, SBERT still provides more novel recommendations than TF-IDF. Depending on the scenario, both models can be used to generate meaningful restaurant recommendations. However, more implicit aspects like a restaurant’s atmosphere can hardly be captured by these models.

Suggested Citation

  • Dante Godolja & Thomas Elmar Kolb & Julia Neidhardt, 2024. "Unlocking the Potential of Content-Based Restaurant Recommender Systems," Springer Proceedings in Business and Economics, in: Katerina Berezina & Lyndon Nixon & Aarni Tuomi (ed.), Information and Communication Technologies in Tourism 2024, pages 239-244, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-58839-6_26
    DOI: 10.1007/978-3-031-58839-6_26
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