研究生: |
江怡岑 Chiang, Yi-Tsen |
---|---|
論文名稱: |
使用魚群演算法之旅遊景點推薦機制 Hierarchical Tour-sites Recommendation Mechanism exploiting Fish-Swarm Algorithm |
指導教授: |
鄭憲宗
Cheng, Sheng-Tzong |
學位類別: |
碩士 Master |
系所名稱: |
電機資訊學院 - 資訊工程學系 Department of Computer Science and Information Engineering |
論文出版年: | 2013 |
畢業學年度: | 101 |
語文別: | 英文 |
論文頁數: | 50 |
中文關鍵詞: | 推薦系統 、群的智慧 、共現理論 、魚群演算法 |
外文關鍵詞: | Recommendation mechanism, swarm intelligence, Co-occurrence, AFSA |
相關次數: | 點閱:54 下載:0 |
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隨著城市文化旅遊的蓬勃發展,許多學者投入提升旅遊經驗之演算法或智慧推薦系統的研究。目前為止的研究多針對個人觀光推薦系統,其中也有專注於降低旅客等待時間或平衡景點雍塞程度的機制,但極少數以群體的角度討論觀光推薦,更少有研究將觀光景點內容和壅塞排解的議題共同考慮。
本篇論文提出一個以旅客群組為單位的旅遊景點推薦機制,此機制分成兩個層次,Inter-site及Intra-site。本機制考慮景點熱門度、旅客的興趣及景點雍塞程度等因素,採用群體智慧-魚群演算法來實作兩個層次的景點推薦方法,並結合共現理論來預測使用者有興趣的景點。本機制致力於降低旅客的平均等待時間以及景點間的平均雍塞程度。是一個將內容、空間、時間皆納入考量之整合型機制。
實驗結果顯示本篇論文提出的方法能夠有效降低旅客參觀多個景點所花的平均等待時間;並透過Inter-site加上共現理論的機制達到城市中各景點的壅塞程度平衡,同時也將旅客的興趣預測加入考量,使整個機制更人性化,更貼近旅客需求。
Recently, there are many researchers investigate about the algorithm or the recommendation system for improving the tourism experience for tourists. Current researches are mostly focusing on the personal tourism recommendation system which reducing the waiting time of tourists. There were few works discuss tourism recommendation based on tourist group, much less considered the context of sites and the congestion reducing issue together. This thesis proposed a hierarchical tour-sites recommendation mechanism based on tourist group which is context, location, and time awareness. This mechanism include two parts, Inter-site and Intra-site, which considering the popularity of sites, the interests of tourists, and the congestion degree of sites. We adopted the Artificial Fish Swarm Algorithm (AFSA) [9] to build this two parts tour-sites recommendation mechanism. In the Inter-site recommendation, we combined Co-occurrence concept to predict the interest of tourists.
This mechanism determined on reducing the average waiting time of tourist and balancing the congestion degree of sites in a city. Moreover, it took the demand of tourists into consideration. The experimental results showed that the mechanism we proposed could reduce the average waiting time of tourist groups in a site; Through Inter-site with co-occurrence mechanism could balance the congestion degree of all sites in a city.
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