Generating Travel Recommendations for Older Adults Based on Their Social Media Activities
Generating Travel Recommendations for Older Adults Based on Their Social Media Activities
复制标题
根据老年人的社交媒体活动为他们提供旅行建议
DOI：
10.1007/978-3-030-77080-8_5
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Konomi Shin’ichi
中科院分区：
文献类型：
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作者：
Lu Yuhong;Taniguchi Yuta;Konomi Shin’ichi
出生率下降和人口老龄化加剧可能会加剧各种社会问题，如社会孤立，这可能对老年人的身心健康产生严重影响。增加外出的频率可以减少未来社会隔离的可能性，并促进从社会隔离中恢复。在本文中，我们提出了一种新的方法，为老年人生成旅游建议，以增加他们的外出频率。该方法基于老年人的社交媒体帖子构建了一个旅游推荐模型。建模过程利用半监督潜在狄利克雷分配（ssLDA）和对象检测技术，通过分析文本和视觉信息中的潜在主题来提取老年人的兴趣。通过匹配潜在主题和旅游目的地的在线信息，可以生成旅游推荐。我们的可行性研究表明，与依赖于传统潜在狄利克雷分配（LDA）模型的基线方法相比，在预测老年人的相关主题方面具有更高的召回率。
The declining birthrate and the increasing aging population can exacerbate various societal issues such as social isolation, which can have a serious impact on the mental and physical health of older adults. Increased frequency of going out can reduce the possibility of future social isolation and facilitate recovery from social isolation. In this paper, we propose a novel method for generating travel recommendations for older adults to increase their frequency of going out. The proposed method builds a travel-recommendation model based on social media posts by older adults. The modelling process exploits the semi-supervised Latent Dirichlet Allocation (ssLDA) and object detection techniques to extract the interests of older adults by analyzing latent topics in textual and visual messages. Travel recommendations can be generated by matching the latent topics and the online information about travel destinations. Our feasibility study demonstrates a higher recall in predicting relevant topics for older adults compared to a baseline method that relies on the conventional Latent Dirichlet Allocation (LDA) model.