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Let pictures speak: hotel selection-recommendation method with cognitive image attribute-enhanced knowledge graphs
International Journal of Contemporary Hospitality Management ( IF 9.1 ) Pub Date : 2024-07-22 , DOI: 10.1108/ijchm-12-2023-1849
Haoqiang Sun , Haozhe Xu , Jing Wu , Shaolong Sun , Shouyang Wang

Purpose

The purpose of this paper is to study the importance of image data in hotel selection-recommendation using different types of cognitive features and to explore whether there are reinforcing effects among these cognitive features.

Design/methodology/approach

This study represents user-generated images “cognitive” in a knowledge graph through multidimensional (shallow, middle and deep) analysis. This approach highlights the clustering of hotel destination imagery.

Findings

This study develops a novel hotel selection-recommendation model based on image sentiment and attribute representation within the construction of a knowledge graph. Furthermore, the experimental results show an enhanced effect between different types of cognitive features and hotel selection-recommendation.

Practical implications

This study enhances hotel recommendation accuracy and user satisfaction by incorporating cognitive and emotional image attributes into knowledge graphs using advanced machine learning and computer vision techniques.

Social implications

This study advances the understanding of user-generated images’ impact on hotel selection, helping users make better decisions and enabling marketers to understand users’ preferences and trends.

Originality/value

This research is one of the first to propose a new method for exploring the cognitive dimensions of hotel image data. Furthermore, multi-dimensional cognitive features can effectively enhance the selection-recommendation process, and the authors have proposed a novel hotel selection-recommendation model.



中文翻译:


让图片说话:认知图像属性增强知识图谱的酒店选择推荐方法


 目的


本文的目的是利用不同类型的认知特征研究图像数据在酒店选择推荐中的重要性,并探讨这些认知特征之间是否存在强化效应。


设计/方法论/途径


这项研究通过多维(浅层、中层和深层)分析,将用户生成的图像“认知”呈现在知识图中。这种方法突出了酒店目的地图像的聚类。

 发现


本研究开发了一种基于知识图谱构建中的图像情感和属性表示的新型酒店选择推荐模型。此外,实验结果表明不同类型的认知特征和酒店选择推荐之间的效果增强。

 实际影响


这项研究通过使用先进的机器学习和计算机视觉技术将认知和情感图像属性纳入知识图中,提高了酒店推荐的准确性和用户满意度。

 社会影响


这项研究加深了对用户生成图像对酒店选择影响的理解,帮助用户做出更好的决策,并使营销人员能够了解用户的偏好和趋势。

 原创性/价值


这项研究是最早提出探索酒店图像数据认知维度的新方法之一。此外,多维认知特征可以有效增强选择推荐过程,作者提出了一种新颖的酒店选择推荐模型。

更新日期:2024-07-19
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