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A Comparative Study of Data-Driven Models for Travel Destination Characterization.

Linus W Dietz1, Mete Sertkan2, Saadi Myftija1

  • 1Department of Informatics, Technical University of Munich, Garching, Germany.

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|April 25, 2022
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Summary

Textual data models best characterize destinations for recommender systems, outperforming venue and factual data. Explicit features can be optimized by learning weights for improved destination characterization.

Keywords:
content-based filteringdata miningdestination characterizationexpert evaluationrank agreement metricsrecommender systems

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Area of Science:

  • Computer Science
  • Information Retrieval
  • Recommender Systems

Background:

  • Content-based recommender systems face challenges in complex domains like travel.
  • Evaluating destination characterization methods is difficult due to the lack of ground truth similarity.
  • Scalability, cost-efficiency, and accuracy are crucial for destination recommendation.

Purpose of the Study:

  • To evaluate the suitability of various data sources for destination characterization.
  • To compare 18 characterization methods across venue, textual, and factual data.
  • To identify data models that best capture desired destination concepts.

Main Methods:

  • Investigated 18 characterization methods across venue, textual, and factual data categories.
  • Utilized rank agreement metrics to compare data models and assess concept capture.
  • Conducted an expert survey to define a desired destination concept for evaluation.

Main Results:

  • Textual data models demonstrated superior performance in characterizing cities.
  • Factual and venue data models were less competitive in capturing underlying concepts.
  • Explicit feature-based data models showed potential for optimization through learned weights.

Conclusions:

  • Textual data sources are most effective for destination characterization in recommender systems.
  • Combining or weighting features from different data sources can enhance characterization accuracy.
  • The study provides insights for selecting optimal data models in tourism recommendation.