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Accommodation Recommendation on Shared Platforms Considering Bidirectional Selection and Review Mechanisms.

  • Published In: International Journal on Artificial Intelligence Tools, 2024, v. 33, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Lin, Yuanyuan; Huang, Chao; Zhang, Xi; Li, Xin; Yao, Wei 3 of 3

Abstract

In recent years, shared accommodation platforms have developed rapidly and become increasingly popular. They commonly adopt unique bidirectional selection and review mechanisms. However, most existing accommodation recommendation strategies overlook the impact of these mechanisms on the recommendation performance. To address this gap, we propose a two-stage recommendation method as Shared Accommodation Recommendation based on Bidirectional Selection and Review mechanisms (SARBSR) to improve the recommendation performance of shared accommodation. The first stage combines a multi-attribute recommendation method (MAR) with a hybrid recommendation method based on similarity and ratings (HRSR) to predict guests' preferences and generate candidate lists. In the second stage, considering bidirectional selection and reviews mechanisms, we assess hosts' willingness to accept guests' requests via trust evaluation and select rooms from the candidate lists as the final recommendation strategy. To evaluate the performance of SARBSR, an empirical study on real-world data from Airbnb is conducted. The results demonstrate the validity of SARBSR and indicate the necessity of considering bidirectional selection and review mechanisms. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal on Artificial Intelligence Tools. 2024/02, Vol. 33, Issue 1, p1
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:0218-2130
  • DOI:10.1142/S0218213023500513
  • Accession Number:175601855
  • Copyright Statement:Copyright of International Journal on Artificial Intelligence Tools is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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