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A travel recommendation system based on social media activity provides a customized place of interest to accommodate user-specific needs and preferences. In general, the user’s inclination towards travel destinations is subject to change over time. In this project, we have analyzed users’ twitter data, as well as their friends and followers in a timely fashion to understand recent travel interest. A machine learning classifier identifies tweets relevant to travel. The travel tweets are then used to obtain personalized travel recommendations. Unlike most of the personalized recommendation systems, our proposed model takes into account a user’s most recent interest by incorporating time-sensitive recency weight into the model. Our proposed model has outperformed the existing personalized place of interest recommendation model, and the overall accuracy is 75.23%.


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Improvising Personalized Travel Recommendation System with Recency Effects

Show Author's information Paromita Nitu1( )Joseph Coelho1Praveen Madiraju2
Department of Mathematical and Statistical Sciences, Marquette University, Milwaukee, WI 53233, USA
Department of Computer Science, Marquette University, Milwaukee, WI 53233, USA

Abstract

A travel recommendation system based on social media activity provides a customized place of interest to accommodate user-specific needs and preferences. In general, the user’s inclination towards travel destinations is subject to change over time. In this project, we have analyzed users’ twitter data, as well as their friends and followers in a timely fashion to understand recent travel interest. A machine learning classifier identifies tweets relevant to travel. The travel tweets are then used to obtain personalized travel recommendations. Unlike most of the personalized recommendation systems, our proposed model takes into account a user’s most recent interest by incorporating time-sensitive recency weight into the model. Our proposed model has outperformed the existing personalized place of interest recommendation model, and the overall accuracy is 75.23%.

Keywords:

travel recommendation, time sensitivity, recency effect, personalization, social media
Received: 02 July 2020 Revised: 29 August 2020 Accepted: 29 October 2020 Published: 12 May 2021 Issue date: September 2021
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Received: 02 July 2020
Revised: 29 August 2020
Accepted: 29 October 2020
Published: 12 May 2021
Issue date: September 2021

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© The author(s) 2021

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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