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Author: 李颼菲
Shofy - Amalia
Thesis Title: 基於趨勢變化與社群影響之使用者喜好探勘機制
Discovering User Preference based on Topical Trends and Social Influences
Advisor: 李漢銘
Hahn-Ming Lee
何建明
Jan-Ming Ho
Committee: 毛敬豪
Ching-Hao Mao
陳培德
Pei-Te Chen
鄧惟中
Wei-Chung Teng
Degree: 碩士
Master
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2014
Graduation Academic Year: 102
Language: 英文
Pages: 40
Keywords (in Chinese): 推薦系統使用者喜好社群影響力時間分配潛藏狄利克里分配
Keywords (in other languages): Recommendation System, User Preference, Social Influence, Time Distribution, Latent Dirichlet Allocation
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  • 社群媒體現存兩個大問題,分別是資訊過多以及如何了解使用者的興趣。依據使用者喜好所建立的推薦系統可同時解決這兩個問題。因此發掘言在喜好資訊將成為了解社群媒體用戶的關鍵。所以我們提出結合時間與社群資訊的推薦系統用於探尋使用者的潛在喜好。


    Social media is facing two big problems in their whole history, which are the information overload and how good they understand the interest of their users. One way to cope with those two problems all at once is applying a recommendation system that fits with user interest. Regarding all that matter, finding latent information has become crucial for better understanding of the user of social media.

    1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Background and Related Works 5 2.1 Recommendation System . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Topic Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.1 Latent Dirichlet Allocation . . . . . . . . . . . . . . . . . . . 8 2.2.2 Gibbs Sampling . . . . . . . . . . . . . . . . . . . . . . . . . 9 3 STARecs: Social and Time-Aware based Recommendation System 15 3.1 Social Influence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2 Time Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.3 Social and Time-Aware based Recommendation System (STARecs) . 18 3.3.1 System Architecture . . . . . . . . . . . . . . . . . . . . . . 19 3.3.2 Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.3.3 Input Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.3.4 Process Data . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.3.5 Output Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4 Evaluation 25 4.1 Experimental Environment . . . . . . . . . . . . . . . . . . . . . . . 25 4.1.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.1.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . 26 4.2 Experimental Design . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.4 Analysis of Experimental Result . . . . . . . . . . . . . . . . . . . . 29 4.5 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 5 Conclusions 35

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