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何婉綺
應用基因演算法於旅遊機制之最佳化研究
應用基因演算法於旅遊機制之最佳化研究
應用基因演算法於旅遊機制之最佳化研究
By
何婉綺
・
Taipei City, Taiwan
Connect
在資通訊科技快速進步下,全球定位系統加速了地理標記服務成長,使用者可以方便在社群媒體中分享目前所在位置以及相關資訊。隨著時間的累積,旅遊相關資料如指數般成長,且使用者對於旅遊景點的要求日漸提高,要如何從這些巨量資料中找到符合使用者需求將值得深入研究。為了瞭解使用者需求與推薦機制間的關係,及避免傳統推薦機制僅針對單一屬性之最佳化演算法,本研究提出應用基因演算法於旅遊機制之最佳化研究。此外,基因演算法(Genetic Alogrithm)透過複製、交配、突變等運算完成一個世代的演化,如此迭代下去以產生適應程度最高的染色體,且其可以隨機多點的同時搜尋,相較其他最佳化演算法的單點循序搜尋方式,可以避面局部最佳解的情況發生,使得可以得到問題的近似最佳解。 首先要探討在旅遊前需要搜尋景點相關資料,從來自各個社交媒體平台上的景點資訊、旅遊評論和照片中得到景點相關資料等數量過多的問題,以及探討在Skyline支配後該如何運用基因演算法推薦之問題。故本研究提出之研究步驟如下: 1. 蒐集Google Place和Foursquare平台上之使用者評分資料,進行資料清洗和資料整合。 2. 先以密度分群法(DBSCAN),對各景點作鄰近景點之篩選。 3. 透過Skyline進行景點支配,作為景點之候選景點。 4. 透過基因演算法將已選擇景點進行交配函數和突變函數,以選出更適景點。
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何婉綺
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Published: Mar 23rd 2020
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