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| Title: | 運用跨城市關係推薦個人化旅遊景點
Personalized Tourist Attractions Recommendation using Cross-City Relationship |
| Authors: | 曾筱筑
Zeng, Siao-Jhu |
| Contributors: | 沈錳坤
Shan, Man-Kwan
曾筱筑
Zeng, Siao-Jhu |
| Keywords: | 景點推薦
主題模型
相關性探勘
Attractions recommendation
Topic modeling
Correlation mining |
| Date: | 2018 |
| Issue Date: | 2018-09-03 15:52:18 (UTC+8) |
| Abstract: | 隨著全球化的發展以及社群媒體的流行，自助旅行的風氣蔚為風潮。由於現在網路上有大量的旅遊資訊，當使用者想規劃一個目的地旅遊時，使用者輸入目的地名稱則會顯示出許多筆資料。然而接收過多的旅遊資訊反而會讓使用者更感困惑，不知如何著手規劃旅遊行程，因此也開始有自動推薦旅遊景點的需求。
與傳統推薦方法相比，旅遊景點推薦需克服沒有實際使用者對旅遊景點的評分，以及使用者拜訪旅遊景點次數少，造成資料稀疏性的問題。在過去旅遊景點推薦的相關研究中，大多只考慮使用者之間潛在的喜好相似度，來改善協同過濾方法中的資料稀疏性問題，較少考慮到旅遊景點潛在的影響力以及不同城市旅遊景點的差異。本研究期望從大量的地理標籤照片中得到使用者旅遊紀錄，透過Latent Dirichlet Allocation (LDA)學習城市中旅遊景點有價值的潛在資訊與使用者對該城市潛在的喜好資訊，再透過Partial Least Square Regression (PLSR)將不同的城市視為不同的領域，找到跨城市之間的關係，為使用者個人化推薦符合該使用者喜好的旅遊景點。
本研究的目標為，當使用者要拜訪目標城市的旅遊景點時，運用考量旅遊次數的使用者拜訪旅遊景點的情形與跨城市之間的關係，推薦符合使用者喜好的目標城市旅遊景點。經過實驗評估，證實本研究與傳統推薦方法相比，能有效提升個人旅遊景點推薦的效能及準確度。
With the development of globalization and the popularity of social media, self-guided tour has become a trend. It takes much time for a user to plan a tour, including searching for attractions. Therefore, the demand for automatic attractions recommendation for self-guided tourist is rising.
Compared with the traditional recommendation mechanism, tourist attractions recommendation needs to overcome no user rating scores for tourist attractions and data sparsity, because most users usually visit only few attractions. In the previous research, most work only considers the similarity between users to improve the data sparsity problem based on collaborative filtering. Less consideration is paid to the correlation of visited attractions between tourist destinations. In this thesis, we collected user travel records from a large number of geo-tagged photos, and utilized Latent Dirichlet Allocation (LDA) to discover the preference distribution of each tourist for each destination. Then, Partial Least Square Regression (PLSR) is employed to find the correlation relationship of preference distributions between tourist destinations. The attractions are personally recommended based on the user’s preferences and the discovered correlation relationships. The experiment shows our proposed method is better than other approaches. |
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[29] 陳逸群, 旅遊行程自動規劃系統的設計與實作. 國立政治大學資訊科學系, 碩士論文, 2016. |
| Description: | 碩士
國立政治大學
資訊科學系
105753010 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0105753010 |
| Data Type: | thesis |
| DOI: | 10.6814/THE.NCCU.CS.011.2018.B02 |
| Appears in Collections: | [資訊科學系] 學位論文
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