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A genetic-based pairwise trip planner recommender system.

Nunung Nurul Qomariyah1, Dimitar Kazakov2

  • 1Computer Science Department, Faculty of Computing and Media, Bina Nusantara University, Jakarta, Indonesia 11480.

Journal of Big Data
|June 7, 2021
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Summary
This summary is machine-generated.

This study introduces a personalized recommender system for e-tourism using a Genetic Algorithm and pairwise preference elicitation. This approach enhances user experience by reducing rating inconsistencies in online travel recommendations.

Keywords:
Genetic algorithmPairwise preferencesPreference learningRecommender system

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Area of Science:

  • Computer Science
  • Information Systems
  • Tourism Technology

Background:

  • The internet's growth presents significant opportunities for businesses in e-tourism.
  • Personalized recommendations are crucial for enhancing user experience in online travel services.

Purpose of the Study:

  • To propose a personalized recommender system for the e-tourism domain.
  • To leverage pairwise preference elicitation combined with a Genetic Algorithm for improved recommendations.

Main Methods:

  • A hybrid approach combining a Genetic Algorithm with pairwise user preference elicitation.
  • User evaluation study with 24 participants.
  • Publication of a Points of Interest (POI) dataset with 201 attractions.

Main Results:

  • The proposed system effectively utilizes pairwise preference elicitation.
  • Pairwise preference elicitation reduces inconsistency and confusion compared to pointwise methods.
  • A dataset of 201 attractions was created and utilized for system evaluation.

Conclusions:

  • The developed recommender system offers a promising approach for personalized e-tourism.
  • Pairwise preference elicitation is a valuable method for improving recommender systems in the tourism sector.
  • The study contributes a novel system and a relevant dataset for e-tourism research.