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研究生:孫育辰
研究生(外文):SUN,YU-CHEN
論文名稱(外文):Developing a Hybrid Collaborative Filtering-based Hotel Recommender System Based on Review Sentiment and Context Information
指導教授:胡雅涵胡雅涵引用關係
指導教授(外文):HU,YA-HAN
口試委員:李珮如蔡志豐
口試委員(外文):LEE,PEI-JUTSAI,CHIH-FONG
口試日期:2017-06-07
學位類別:碩士
校院名稱:國立中正大學
系所名稱:資訊管理系研究所
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:105
語文別:英文
論文頁數:58
中文關鍵詞:推薦系統協同過濾情感分析
外文關鍵詞:Recommender systemCollaborative filteringSentiment analysis
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過去數十年間,旅遊產業快速發展,許多人會利用假日期間規劃出門遊玩的行程,對這些遊客來說如何挑選適合的旅館對他們是很重要的議題,另一方面,網際網路快速發展,網路上存在非常多的資訊,造成了資訊超載(Information overload)的問題,遊客沒辦法有效率的取得並過濾出他們想要的資訊,因此旅館推薦系統(Recommender Systems, RS)就扮演一個很重要的角色,推薦適合的旅館給遊客,但是要發展一個推薦系統必須要克服資料稀疏(Data sparsity)的問題。
協同過濾(Collaborative Filtering, CF)是推薦系統中最常使用的技術之一,另外,情境感知推薦系統 (Context-aware Recommender System, CARS)是一種被廣泛使用的推薦系統,它除了運用協同過濾的技術,另外也考慮了使用者當下做決策的情境,更加貼近真實生活情況,但同樣面臨非常嚴重的資料稀疏問題。
為了解決資料稀疏的問題,我們的研究發展了個人化旅館推薦系統基於遊客評論的情感以及情境資訊,使用遊客的旅遊型態作為情境資訊,並且以遊客的評論做情感分析(Sentiment analysis, SA),從中萃取出遊客隱藏的喜好,以解決情資料稀疏的問題,本研究透過Tripadvisor.com 的資料來進行實驗,實驗結果顯示本研究提出的個人化旅館推薦系統達到更好的預測效果。

Over the decades, the tourism industry has experienced rapid growth. Many people plan on having a trip during their vacation. It is an important issue for them to choose a suitable hotel. Meanwhile, since the rapid development of the internet, the amount of online information on hotel has increased dramatically causing the information overload. Travelers can’t find the information really helpful to them in an efficient way. As a result, hotel recommender system (RS) plays an important role to recommend appropriate hotels to the travelers. However, if we want to develop a RS, we must overcome the data sparsity problem.
Collaborative Filtering (CF) is one of the most common technique used in RS. Context-aware Recommender System (CARS) is a kind of RS which is widely used. It not only utilizes CF technique but also considers the context information while travelers making decisions. CARS is more similar to real situation in daily life, but it also faces data sparsity problem.
In order to solve the data sparsity problem, our research developed a personalized hotel recommender system based on review sentiment and context information. We viewed travel types as context information. Based on the travelers’ reviews, we utilized SA technique to discover the hidden preferences of travelers to address data sparsity problem. Our experiment datasets were collected from Tripadvisor.com. After the experiments, we find out that our personalized hotel recommender system reaches better performances.

1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Research objective 3
1.4 Results and contribution 4
1.5 Organization 4
2 Literature Review 5
2.1 Recommender System (RS) 5
2.1.1 Sentiment Analysis (SA) on recommendation 7
2.1.2 Context-Aware Recommender System (CARS) 8
2.2 Hotel Recommendation 11
3 Research method 15
3.1 Data collection and context partition 16
3.2 Review preprocessing 17
3.2.1 Spell checking 17
3.2.2 POS tagging 18
3.2.3 Hotel features definition 19
3.3 Imputation using ICF 20
3.3.1 Finding travelers’preference through sentiment analysis 20
3.3.2 Hotel-hotel similarity calculation 22
3.3.3 Neighborhood selection and Predicted scores imputation 23
3.4 User-based Collaborative filtering 24
3.4.1 Traveler-traveler similarity calculation 24
3.4.2 Neighborhood selection and final prediction 25
4 Evaluation Method 26
4.1 Dataset description 26
4.2 Evaluation design and performance measurement 27
5 Experiment result 28
5.1 Experiment 1 28
5.2 Experiment 2 29
5.3 Experiment 3 36
6 Conclusion and Future work 49
6.1 Conclusion 49
6.2 Future work 50
7 Reference 51


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