评估酒店评论对信息过载的帮助程度:一种多视角空间特征方法

IF 6.3 3区 管理学 Q1 HOSPITALITY, LEISURE, SPORT & TOURISM
Yang Liu, Xingchen Ding, Maomao Chi, Jiang Wu, Lili Ma
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引用次数: 0

摘要

由于缺乏评论内容的语义和空间特征,消费者对有用性的看法仍然是一个悬而未决的问题。本文旨在探讨酒店点评内容的三个方面:时间、评级和地点,以评估酒店点评的有用性。设计了一种多视图图卷积网络(MVGCN)和捕获多模态语义信息的注意机制。对Yelp和TripAdvisor上的实验结果进行了评价。研究结果表明,这有助于过滤有用的信息,避免信息过载时,向客户阅读。结果表明,提出的模型优于基线,并说明了模型在每个视图中的可解释性。我们的工作对于酒店和旅游平台的专业人士来说至关重要,他们可以利用我们的发现来优化他们的销售系统。同时,搜索结果可以帮助访问者或用户获取有益的信息,避免信息过载。本研究是为数不多的能够促进信息超载模型的文章之一,该模型旨在指导评估酒店行业评论的有用性的研究。本研究还通过开发多模态数据的提取特征,提供具有几种新评估的多视图特征以及涉及深度学习的新框架,为方法做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Assessing the helpfulness of hotel reviews for information overload: a multi-view spatial feature approach

Assessing the helpfulness of hotel reviews for information overload: a multi-view spatial feature approach

Consumer perceptions of helpfulness remain an open question due to the lack of semantic and spatial features of review content. This paper aims to explore three aspects of the contents of a review: time, rating, and location, to assess the helpfulness of hotel reviews. A multi-view graph convolutional network (MVGCN) and attention mechanisms that capture multimodal semantic information are designed. The experimental results on Yelp and TripAdvisor are evaluated. The findings indicate that this facilitates the filtering of helpful information and avoids information overload when reading to customers. The results show that the proposed model outperforms the baseline and illustrates the interpretability of the models in each view. Our work is essential for professionals of both hotel and travel platforms that can utilize our findings to optimize their sales systems. Also, the results can help visitors or users acquire beneficial information and avoid information overload. This study is one of the few articles that can promote a model interpretable for information overload, which aims to guide research on evaluating the helpfulness of reviews in the hotel sector. This study contributes also to the methodology by developing extracting features of multimodal data, giving a multi-view feature with several novel assessments, and a novel framework involving deep learning.

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来源期刊
Information Technology & Tourism
Information Technology & Tourism HOSPITALITY, LEISURE, SPORT & TOURISM-
CiteScore
18.10
自引率
5.40%
发文量
22
期刊介绍: Information Technology & Tourism stands as the pioneer interdisciplinary journal dedicated to exploring the essence and impact of digital technology in tourism, travel, and hospitality. It delves into challenges emerging at the crossroads of IT and the domains of tourism, travel, and hospitality, embracing perspectives from both technical and social sciences. The journal covers a broad spectrum of topics, including but not limited to the development, adoption, use, management, and governance of digital technology. It supports both theory-focused research and studies with direct relevance to the industry.
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