Enhancing Travel Decision-Making: A Contrastive Learning Approach for Personalized Review Rankings in Accommodations
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866911938704834560 |
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| author | Igebaria, Reda Fainman, Eran Mizrachi, Sarai Beladev, Moran Wang, Fengjun |
| author_facet | Igebaria, Reda Fainman, Eran Mizrachi, Sarai Beladev, Moran Wang, Fengjun |
| contents | User-generated reviews significantly influence consumer decisions, particularly in the travel domain when selecting accommodations. This paper contribution comprising two main elements. Firstly, we present a novel dataset of authentic guest reviews sourced from a prominent online travel platform, totaling over two million reviews from 50,000 distinct accommodations. Secondly, we propose an innovative approach for personalized review ranking. Our method employs contrastive learning to intricately capture the relationship between a review and the contextual information of its respective reviewer. Through a comprehensive experimental study, we demonstrate that our approach surpasses several baselines across all reported metrics. Augmented by a comparative analysis, we showcase the efficacy of our method in elevating personalized review ranking. The implications of our research extend beyond the travel domain, with potential applications in other sectors where personalized review ranking is paramount, such as online e-commerce platforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00787 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Travel Decision-Making: A Contrastive Learning Approach for Personalized Review Rankings in Accommodations Igebaria, Reda Fainman, Eran Mizrachi, Sarai Beladev, Moran Wang, Fengjun Information Retrieval Machine Learning User-generated reviews significantly influence consumer decisions, particularly in the travel domain when selecting accommodations. This paper contribution comprising two main elements. Firstly, we present a novel dataset of authentic guest reviews sourced from a prominent online travel platform, totaling over two million reviews from 50,000 distinct accommodations. Secondly, we propose an innovative approach for personalized review ranking. Our method employs contrastive learning to intricately capture the relationship between a review and the contextual information of its respective reviewer. Through a comprehensive experimental study, we demonstrate that our approach surpasses several baselines across all reported metrics. Augmented by a comparative analysis, we showcase the efficacy of our method in elevating personalized review ranking. The implications of our research extend beyond the travel domain, with potential applications in other sectors where personalized review ranking is paramount, such as online e-commerce platforms. |
| title | Enhancing Travel Decision-Making: A Contrastive Learning Approach for Personalized Review Rankings in Accommodations |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2407.00787 |