藉由探勘照片分享之社群媒體推薦旅程

Tour Recommendations by Mining Photo Sharing Social Media

孫至緣(Chih-Yuan Sun)

指導教授 ： 李瑞庭

國立臺灣大學/管理學院/資訊管理學研究所/碩士(2016年)

https://doi.org/10.6342/NTU201702928

摘要

隨著分享照片及影片社群網站的興起，越來越多使用者分享他們的照片或影片給他們的家人及朋友。因此，在本研究中，我們提出了一個研究架構，透過照片分享網站中使用者產生的資料，推薦符合使用者興趣及時間需求的旅遊行程。首先，我們採用平均移動分群演算法，將所收集的地點分成地標，再將地標合併成區域。接著，我們採用隱含狄利克雷分佈模型，將具有相似主題的地標分在一起。然後，我們計算每個使用者到訪每個地標以及區域的偏好(分數)，在計算分數時，我們會考量到地標的熱門程度、地標跟使用者在主題上的相似程度，以及相似的使用者到訪地標的比例。最後，根據每個地標與區域的分數，我們提出一個有效率的演算法，推薦k個最高分數的旅遊行程給使用者。多數過往的研究皆以地標為單位來推薦行程，而我們的架構則以區域為單位來進行推薦，因此，可以避免推薦出會在區域間往返的繞路行程。這樣不僅可節省交通時間，也讓使用者有更多時間能到訪更多的地點。實驗結果顯示，我們的方法無論在推薦行程的平均分數或推薦的精確度都勝過Markov-Topic方法。我們所提出的架構，可幫助使用者們規劃理想的旅遊行程，並且針對不同型態的使用者，客製化合適的行程。

關鍵字

旅程推薦 ； 照片分享社群網站 ； 平均移動分群演算法 ； 隱含狄利克雷分佈模型 ； 資料探勘

並列摘要

With the increasing popularity of photo and video sharing social networks, more and more people have shared their photos or videos with their family members and friends. Therefore, in this study, we propose a framework for recommending travel tours to meet user’s individual interest and time-awareness by using user-generated contents in a photo sharing social network. The proposed framework contains four phases. First, we cluster geotagged locations into landmarks, and further clusters these landmarks into areas by mean-shift clustering method. Second, we employ the Latent Dirichlet Allocation model to cluster together similar landmarks. Third, to recommend tours for a user, we compute the tendency (or score) of visiting each landmark by the landmark popularity, attraction of landmark to the user, and how many users similar to the user visit the landmark. Finally, based on the scores computed, we develop an efficient method to recommend top-k tours with highest scores for the user. Unlike most previous methods recommending tours landmark by landmark, our framework recommends tours area by area so that users can avoid going back and forth from one area to another and save plenty of time on transportation, which in turn can visit more landmarks. The experiment results show that our proposed method outperforms the Markov-Topic method in terms of average score and precision. Our proposed framework may help users plan their trips and customize a trip for each individual.

並列關鍵字

tour recommendation ； photo sharing social network ； mean-shift clustering method ； Latent Dirichlet Allocation model ； data mining

參考文獻

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