以人格特質與質量擴散方法建立旅遊景點推薦模型;Recommendation Model for Tourism by Personality Type Using Mass Diffusion Method

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题名:	以人格特質與質量擴散方法建立旅遊景點推薦模型;Recommendation Model for Tourism by Personality Type Using Mass Diffusion Method
作者:	陳又暄;Chen, Yu-Hsuan
贡献者:	企業管理學系
关键词:	旅遊景點推薦;大五人格;MBTI 16型人格;質量擴散方法;Tourism Recommendation;Big Five Factor;MBTI 16 Personality Type;Mass Diffusion Method
日期:	2021-07-12
上传时间:	2021-12-07 11:34:10 (UTC+8)
出版者:	國立中央大學
摘要:	推薦系統研究的主題遍布於各種領域，常見的如網路零售、電影、書籍……等。其中，旅遊相關的推薦系統也是廣為研究的主題之一。許多旅遊相關的推薦系統研究是使用協作過濾 (Collaborative Filtering) 方法，並試著在推薦系統方法加入人格特質以提升準確率。Zhou 等人 ( 2007) 認為質量擴散 (Mass Diffusion) 方法比協作過濾方法更加準確，不過此方法多是應用在推薦電影類型或是書籍等，較少應用在旅遊相關領域。針對質量擴散方法的研究，相比於其他的推薦系統方法，較少研究是考慮到人格特質，如大五人格或是 MBTI 16 型人格。本研究利用質量擴散方法建立旅遊景點推薦模型，並結合其他推薦系統研究中常用到的人格特質，大五人格和MBTI 16型人格，來達到旅遊景點個性化推薦。根據本研究實驗結果顯示，相比於協作過濾方法結合人格特質，質量擴散方法結合人格特質可以更精確地推薦景點給使用者。;Recommendation systems are studied in various fields, such as e-tailing, movies, books, ......, and so on. Among them, tourism recommendation systems are also one of the widely research topics. Many tourism recommendation system studies use Collaborative Filtering method and try to add personality traits to the recommendation system methods to improve the precision. Zhou et al. (2007) suggested that Mass Diffusion method has more precision than Collaborative Filtering method, but this method is mostly applied to recommending movie genres or books, but less often in tourism. Compared to other recommendation systems, fewer studies have taken into account personality traits such as Big Five Factor and MBTI 16 Personality Type. In this study, we used the Mass Diffusion method to establish a model of tourism attraction recommendation, and combined the personality traits commonly used in other recommendation system studies, such as Big Five Factor and MBTI 16 Personality Type, to achieve personalized recommendation of tourism attractions. According to the experimental results of this study, compared with the Collaborative Filtering method combined with personality traits, the Mass Diffusion method combined with personality traits can recommend attractions to users more accurately.
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