A genetic‐based pairwise trip planner recommender system

Nunung Nurul Qomariyah, Dimitar Lubomirov Kazakov

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The massive growth of internet users nowadays can be a big opportunity for the busi- nesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-tourism. In this paper, we propose an approach of personalized recommender system with pairwise preference elicitation for the e-tourism domain area. We used a combination of Genetic Agorithm with pairwise user prefer- ence elicitation approach. The advantages of pairwise preference elicitation method, as opposed to the pointwise method, have been shown in many studies, including to reduce incosistency and confusion of a rating number. We also performed a user evaluation study by inviting 24 participants to examine the proposed system and publish the POIs dataset which contains 201 attractions used in this study.
Original languageEnglish
Article number77
Number of pages23
JournalJournal of Big Data
Issue number77
Publication statusPublished - 30 May 2021

Bibliographical note

© The Author(s) 2021


  • recommender systems
  • pairwise choice
  • genetic algorithm
  • preference learning

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