Automated photo filtering for tourism domain using deep and active learning: the case of Israeli and worldwide cities on instagram

IF 6.3 3区 管理学 Q1 HOSPITALITY, LEISURE, SPORT & TOURISM
Abigail Paradise-Vit, Aviad Elyashar, Yarden Aronson
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引用次数: 0

Abstract

Social media platforms like Instagram significantly influence tourists’ travel decisions by providing them with valuable insights, recommendations, authentic information, and points of interest. However, photos shared with location-specific hashtags, even those related to tourist attractions, do not always reflect the actual destination, creating challenges for potential visitors seeking accurate information. To assist tourists in finding pertinent tourism information for specific destinations, we propose VISTA: Visual Identification of Significant Travel Attractions. The proposed method employs deep learning and active learning techniques to automatically classify photos into: ‘Tourism-Related’ photos (i.e., photos related to tourism) and ‘Non-Tourism-Related’ photos (i.e., photos unrelated to tourism). To train our machine learning classifier, we created a dataset containing photos of the 10 most popular Israeli cities on Instagram. The classifier obtained an accuracy score of 0.965 and a weighted F1 score of 0.964. Evaluating our classifier’s global generalization on the InstaCities100K dataset, derived from InstaCities1M, yielded an accuracy score of 0.958 and a weighted F1 score of 0.959. The effectiveness of VISTA was demonstrated by comparing tourism-related and non-tourism-related photos in terms of photo proportion, user engagement, and object comparison. We found that most photos published on Instagram associated with cities are irrelevant to tourists and that tourism-related photos received more likes than non-tourism-related photos. Finally, there was a low overlap between objects in the two photo collections. Based on these results, we conclude that VISTA can help tourists tackle the problem of finding relevant tourism-related photos among the high volume of photos available on Instagram.

Abstract Image

利用深度学习和主动学习为旅游领域自动过滤照片:Instagram 上的以色列和世界城市案例
Instagram 等社交媒体平台通过向游客提供有价值的见解、建议、真实信息和兴趣点,极大地影响了游客的旅行决策。然而,使用特定地点标签分享的照片,即使是与旅游景点相关的照片,也并不总能反映实际目的地的情况,这给潜在游客寻求准确信息带来了挑战。为了帮助游客找到特定目的地的相关旅游信息,我们提出了 "VISTA:重要旅游景点的视觉识别"。该方法采用深度学习和主动学习技术,自动将照片分类为旅游相关 "照片(即与旅游相关的照片)和 "非旅游相关 "照片(即与旅游无关的照片)。为了训练机器学习分类器,我们创建了一个数据集,其中包含 Instagram 上最受欢迎的 10 个以色列城市的照片。分类器的准确率为 0.965,加权 F1 得分为 0.964。在由 InstaCities1M 衍生的 InstaCities100K 数据集上评估分类器的全局泛化能力时,准确率为 0.958,加权 F1 得分为 0.959。通过比较旅游相关照片和非旅游相关照片在照片比例、用户参与度和对象比较方面的表现,证明了 VISTA 的有效性。我们发现,Instagram 上发布的大多数与城市相关的照片都与游客无关,而与旅游相关的照片比与旅游无关的照片获得了更多的点赞。最后,两组照片中的对象重合度较低。基于这些结果,我们得出结论:VISTA 可以帮助游客解决从 Instagram 上的大量照片中找到相关旅游照片的问题。
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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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