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研究生:黃耀慶
研究生(外文):Yao-qing Huang
論文名稱:應用共享平台Airbnb的使用者偏好於個人化之推薦系統
論文名稱(外文):LDA-Based Personalized recommendation for Airbnb
指導教授:林耕霈
指導教授(外文):Keng-Pei Lin
學位類別:碩士
校院名稱:國立中山大學
系所名稱:資訊管理學系研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2017
畢業學年度:106
語文別:英文
論文頁數:49
中文關鍵詞:Airbnb推薦系統文字探勘隱含狄利克雷分布主題模型
外文關鍵詞:AirbnbRecommender systemText miningLDATopic model
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Airbnb是目前在住宿業裡最成功的共享經濟平台代表。雖然評論系統的建立讓每位房客留下體驗後的評論對未來預計入住的旅客有很大的幫助,但是長久下來累積的大量文字資訊,卻也同時提高了房屋挑選上的困難,想要經由過往的評論找到適合的房屋所需耗費的心力比以往更多。
在本篇論文中,我們以LDA主題模型為基礎建立Airbnb個人化推薦系統。用主題分布的方式呈現每間房屋的特色,同時也透過房客的住宿紀錄建立每位房客各自的主題分布,以代表每位房客的偏好。透過計算房客和房屋之間的主題相似度降冪產生推薦清單,推薦數個該房客可能喜愛的房屋。
在實驗階段,我們透過召回率(Recall)衡量推薦系統的表現。首先以LDA為基礎的方法和現行常見方法進行比較,以及比較不同文字資訊作為來源的推薦系統表現如何。最後實驗結果顯示,資料來源同時採用房客評論和留言分數都有助於我們以LDA為基礎的系統推薦表現。
Airbnb is one of the most successful sharing economy platforms in the hospitality industry. Although the availability of large-scale reviews can be beneficial but it is more difficult in the decision-making process, because of the huge amount of reviews which make guests confused in selecting the best possible and suitable properties.
In this thesis, we propose a personalized recommender system by applying LDA to extract latent topics of textual resource of each property and use the probability of topic distribution to represent the features of each property. Further, construct guest profile based on guest’s historical records in order to realize guest preference. Finally, for each candidate property, we consider the profiles of property and guest to estimate a sorted recommend list for the guest.
For the evaluation, we adopt Recall to evaluate the recommendation performance. The experimental result shows that our LDA-based model performs better than the baseline. Afterwards, we compare the performance among different textual information which shows the review and rating score are appropriate resource for the property representation and guest preference on the LDA-based personalized recommender system.
CHAPTER 1-Introduction 1
1.1. Background and Motivation 1
1.2. Results and Contribution 5
1.3. Overall Architecture 6
CHAPTER 2-Literature Review 7
2.1. Content-Based Recommender Systems 7
2.2. Latent Dirichlet Allocation 8
CHAPTER 3-Methodology 10
3.1. Research Process 10
3.2. Data Collection 13
3.3. Data Preprocessing 14
3.4. Property Representation 17
3.5. Guest Profile Generation 19
3.6. Recommend Properties to Guest 22
CHAPTER 4-Empirical Evaluation 24
4.1. Dataset description 25
4.2. Experimental Settings 27
4.3. Evaluation metric 28
4.4. Result and discussion 30
CHAPTER 5-Conclusion and Future work 35
References 37
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