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研究生:蘇盈如
研究生(外文):Su, Ying-Ju
論文名稱:基於評論探勘與使用者偏好因素分析之評分預測方法比較
論文名稱(外文):A Comparison of Rating Prediction Methods based on Review Mining and User Preference Factor Analysis
指導教授:劉敦仁劉敦仁引用關係
指導教授(外文):Liu, Duen-Ren
口試委員:劉敦仁羅濟群周世傑
口試委員(外文):Liu, Duen-RenLo, Chi-ChunChou, Shi-Chie
口試日期:2017-07-17
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊管理研究所
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:2018
畢業學年度:106
語文別:英文
論文頁數:39
中文關鍵詞:推薦系統評分預測隱含主題模式面向語意協同過濾內容式過濾語意分析文字探勘
外文關鍵詞:recommender systemRating predictionLatent Dirichlet AllocationAspect-based SemanticsSemantic analysisText mining
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  • 被引用被引用:0
  • 點閱點閱:251
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  • 下載下載:4
  • 收藏至我的研究室書目清單書目收藏:0
線上評論網站不僅是一個分享店家資訊及自身消費經驗的平台,更提供了評論及評分功能,足以間接影響其他使用者的消費行為。然而,大量的線上資訊導致信息過載的問題,在此情況下,使用者很難有效地過濾有用的資訊。因此,如何透過評論、評分及店家資訊,多層面地分析使用者消費偏好並預測使用者評分,已為建立個人化推薦系統之重要議題。
過去推薦系統大多以協同過濾的方法,僅考量使用者的歷史評分記錄,將無法得知使用者在各個面向的重視程度及評分習慣,導致難以有效地預測使用者評分。有鑑於此,本研究提出並比較基於評論探勘與使用者偏好因素分析之多種使用者評分預測方法,分析使用者對不同面向的偏好、評分習慣以及商家表現,進而預測使用者對不同商家的評分,做為建立推薦系統的依據。本研究以Yelp全球性商店評論網站的資料進行實驗,實驗結果顯示所提方法能夠提升預測使用者評分的準確率。
Online review websites not only allow users to share business information and consumer experience, but also the ability to rate, review and influence one another. They help users decide whether to buy products or visit business stores indirectly. However, users are difficult to filter out useful information efficiently due to the overload from a large amount of review information. Accordingly, in order to make accurate predictions for personalizing recommendation systems, it is an important issue to analyze user preferences and predict user ratings by analyzing the review opinions, ratings and business information on the websites.
Traditional rating prediction methods usually adopt collaborative filtering to predict user ratings based on historical rating records. These methods, which only consider historical user ratings and ignore user preference including aspect-based preference and rating behavior, are limited and not effective in predicting user ratings. Therefore, in this research, we propose and compare several user rating prediction methods based on review mining and analysis of user preference factors. The methods are applied to analyze user aspect-based preference, rating behavior and business reputation, then to predict user ratings on business stores for generating an efficient recommendation system. Yelp dataset is used in the experiments. The experiment results show that the proposed methods can improve the accuracy of rating predictions.
摘要 i
ABSTRACT ii
致謝 iii
List of Tables vi
List of Figures vii
1 Introduction 1
1.1 Research Background and Motivation 1
1.2 Research Objective 4
1.3 Organization 6
2 Related Work 7
2.1 Recommendation Approaches 7
2.2 Collaborative Filtering 7
2.2.1 User- based CF 8
2.2.2 Item-based CF 8
2.3 Matrix Factorization 9
2.4 Aspect Detections 10
2.4.1 Probability Latent Semantic Analysis (pLSI/pLSA) 10
2.4.2 Latent Dirichlet Allocation 11
2.4.3 Local LDA 12
3. Proposed Approach 13
3.1 Overview 13
3.2 Review Feature Extraction 14
3.2.1 Aspect Detection 14
3.2.2 Semantic Analysis 15
3.3 User Preference 16
3.3.1 User Rating Behavior Analysis (URB) 16
3.3.2 Business Reputation Analysis (BR) 17
3.4 User Preference Model 17
3.5 Clustering User Preference Model 19
3.6 Business Performance Analysis 20
3.7 Rating prediction 22
4. Experiment and Evaluation 23
4.1 Dataset Data description 23
4.2 Evaluation measurements 24
4.3 Evaluation of rater filtering 24
4.4 Evaluation of considering URH and BR 27
4.5 Evaluation of different business performance calculation methods 28
4.6 Evaluation for topic number of LDA 29
4.7 Evaluation of cluster number 31
4.8 Parameter adjust by Random Search 33
4.9 Discussion 33
5. Conclusions 35
References 37
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