智慧活躍老化之實現：以資料驅動為基礎之AI長者在地交友推薦模式;AI Makes Active Aging a Reality: A Data-Driven AI Elders′ Local Friend Recommendation Model

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Title:	智慧活躍老化之實現：以資料驅動為基礎之AI長者在地交友推薦模式;AI Makes Active Aging a Reality: A Data-Driven AI Elders′ Local Friend Recommendation Model
Authors:	蔡知耘;Tsai, Chih-Yun
Contributors:	資訊管理學系
Keywords:	活躍老化;人工智慧;人格特質;深度學習;機器學習;社會參與;Active ageing;Artificial intelligence;Personality traits;Deep learning;Machine learning;Social participation
Date:	2022-09-07
Issue Date:	2022-10-04 12:04:57 (UTC+8)
Publisher:	國立中央大學
Abstract:	高齡化是全球社會正面臨的挑戰，隨之引發而來勞動力短缺、社會成本遽增及醫療能量與資源不足等社會問題的產生。長者的妥善照顧會是一個社會安定的首要力量之展現。然而，如何妥善照顧長者一直都是各領域待解決的問題。長者的妥善照顧絕不只是衣食無虞，富足的心靈基礎更是需要各方協助與努力。因此，本研究以資料驅動為基礎，透過人工智慧技術，為長者建構—以在地交友推薦模式為基礎的社區人際網路，增加長者對於社會的羈絆性，提升長者的社會參與，擴大其交友圈，促進老化過程的活躍性，減少孤獨老的可能。本研究包括兩階段實驗，第一階段以深度學習技術分析長者生活語料隱含之人格特質，以克服過去以傳統問卷量表在評估人格特質需要耗費大量時間的困難。第二階段則以機器學習技術，結合長者人格特質、興趣愛好及在地例行性到訪的地理資訊，發展基於地理空間與個人特質相似性之長者媒合推薦模型，研究結果將以資訊科技輔助的方式，藉由深度學習更有效的分析方法，結合實務的推薦方案，使社區活躍度提高，讓長者更願意走出家門，結識更多的朋友，擁有更多社會參與的機會，並從過程中達到活躍老化的目標。;Aging is a challenge facing the global society, which leads to labor shortage, social cost increase, and lack of medical energy and resources. The proper care of the elderly is the primary force behind the stability of a society. However, the proper care of the elderly has always been an issue that needs to be addressed in various fields. The proper care of the elderly is not only about food and clothing, but also about the spiritual foundation that requires assistance and efforts from all parties. Therefore, this study uses data-driven technology to construct an interpersonal community network for the elderly based on the local friendship recommendation model, to increase the social bonding of the elderly, to enhance their social participation, to expand their friendship circle, to promote the activity of the aging process, and to reduce the possibility of lonely aging. In the first stage, we used deep learning techniques to analyze the personality traits implied by the elderly′s life data, in order to overcome the time consuming difficulties in assessing personality traits by traditional questionnaires. In the second stage, we use machine learning technology to combine the personality traits, interests and hobbies of the elderly with the geographic information of routine visits in the local area to develop a recommendation model based on the similarity of geospatial and personal traits of the elderly. The results of the study will be used to increase the activity of the community by combining a more effective analysis method with a practical recommendation program.
Appears in Collections:	[Graduate Institute of Information Management] Electronic Thesis & Dissertation

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