算法过滤是否会导致在线旅游信息搜索出现过滤泡沫?

IF 6.3 3区 管理学 Q1 HOSPITALITY, LEISURE, SPORT & TOURISM
Yaqi Gong, Ashley Schroeder, Bing Pan, S. Shyam Sundar, Andrew J. Mowen
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引用次数: 0

摘要

当游客在网上搜索信息时,个性化算法往往会根据上下文过滤海量信息,为他们提供信息子集,以提高相关性,避免信息过载。然而,人们对这些算法的阴暗面关注有限。对个性化算法的一个有影响力的批评是 "过滤泡沫效应",这一假说认为,人们会根据自己先前的在线活动被孤立在自己的信息泡沫中,从而导致视野狭窄,发现新体验的机会减少。因此,一个重要的问题是,算法过滤是否会导致过滤泡沫。我们采用三维 "级联 "旅游决策模型,通过两步实验在在线旅游信息搜索中对这一问题进行了实证探索。我们用两极化的 YouTube 视频训练两个虚拟代理,并操纵它们在谷歌搜索中从站外和站内地理位置进行旅游信息搜索。我们收集了前三页的搜索结果,并通过两个数学指标和后续内容分析进行了分析。结果显示,两个虚拟代理在事先接受两极化培训后并无明显差异。但是,当搜索地理位置从站外变为站内时,39%-69% 的搜索结果会发生变化。此外,不同搜索词之间的差异也不同。总之,我们的数据表明,虽然算法过滤在检索相关搜索结果方面很稳健,但并不一定显示出过滤泡沫的证据。本研究提供了理论和方法论方面的启示,以指导今后对在线旅游信息搜索中的过滤泡沫和语境个性化的研究。此外,还讨论了营销方面的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Does algorithmic filtering lead to filter bubbles in online tourist information searches?

Does algorithmic filtering lead to filter bubbles in online tourist information searches?

When tourists search information online, personalization algorithms tend to contextually filter the vast amount of information and provide them with a subset of information to increase relevance and avoid overload. However, limited attention is paid to the dark side of these algorithms. An influential critique of personalization algorithms is the filter bubble effect, a hypothesis that people are isolated in their own information bubble based on their prior online activities, resulting in narrowed perspectives and fewer discovery of new experiences. An important question, therefore, is whether algorithmic filtering leads to filter bubbles. We empirically explore this question in an online tourist information search with the three-dimensional ‘cascade’ tourist decision-making model in a two-step experiment. We train two virtual agents with polarized YouTube videos and manipulate them to conduct travel information searches from both off-site and on-site geolocations in Google Search. The first three pages of search results are collected and analyzed with two mathematical metrics and follow-up content analysis. The results do not show significant differences between the two virtual agents with polarized prior training. However, when search geolocations change from off-site to on-site, 39–69% of the search results vary. Additionally, this difference varies between search terms. In summary, our data show that while algorithmic filtering is robust in retrieving relevant search results, it does not necessarily show evidence of filter bubbles. This study provides theoretical and methodological implications to guide future research on filter bubbles and contextual personalization in online tourist information searches. Marketing implications are discussed.

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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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