Is it a work or leisure travel? Applying text classification to identify work-related travel on social networks

TL;DR

This paper develops a text classification model using advanced NLP techniques like BERT, RoBERTa, and BART to distinguish between work-related and leisure travel from social network data, aiming to improve personalized travel recommendations.

Contribution

It introduces a novel application of state-of-the-art ATC models to identify travel purpose, enhancing recommender systems for social network-based travel data.

Findings

High accuracy in classifying travel purpose

Improved recommendation relevance for users

Effective use of BERT, RoBERTa, and BART models

Abstract

In today's digital era, the use of Social Networks (SNs) and Location-Based SNs (LBSNs) has become integral for travelers seeking Points of Interest (POI) and sharing travel experiences. This trend is supported by the fact that a significant majority of American travelers utilize SNs during their trips. However, the abundance of information available on these platforms presents a challenge in identifying the best options. To address this issue, Recommender Systems (RS) are commonly employed to suggest POIs based on user history, with the integration of contextual information enhancing the quality of recommendations. Notably, incorporating user travel purpose, which is often overlooked but holds potential in characterizing travelers' behavior, can lead to more tailored recommendations. In this study, we propose a model to predict whether a trip is leisure or work-related, utilizing state-of-the-art Automatic Text Classification (ATC) models such as BERT, RoBERTa, and BART to enhance the understanding of user travel purposes and improve recommendation accuracy in specific travel scenarios.