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Generating Travel Recommendations for Older Adults Based on Their Social Media Activities

机译:基于社交媒体活动为老年人提供旅行建议

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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.
机译:出生率下降和增加的人口越来越大的人口可以加剧社会孤立等各种社会问题,这可能对老年成年人的心理和身体健康产生严重影响。出门的增加可以减少未来社会隔离的可能性,并促进社会隔离的恢复。在本文中,我们提出了一种新的方法,可以为老年人发电旅行建议,以提高他们出门的频率。该方法基于老年人的社交媒体帖子建立旅行建议模型。建模过程利用半监督潜在的Dirichlet分配(SSLDA)和对象检测技术来通过分析文本和可视消息中的潜在主题来提取旧成年人的利益。通过匹配潜在主题和有关旅行目的地的在线信息,可以生成旅行建议。与依赖于传统潜在Dirichlet分配(LDA)模型的基线方法相比,我们的可行性研究表明,预测老年人的相关主题更高的召回。

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