在社群網站上作互動推薦及研究使用者行為對其效果之影響

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姓名

羅耀宗(Yao-Chung Lo) 查詢紙本館藏

畢業系所

企業管理研究所

論文名稱

在社群網站上作互動推薦及研究使用者行為對其效果之影響
(Implementing interaction recommendations on social networking sites and investigating how user behavior influences their effectiveness)

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摘要(中)

雖然許多學者已經根據社群網站上使用者產生的內容所推斷的個人興趣，發展出各式各樣的推薦系統，極少學者體認到可以利用這些社群媒體獨有的特徵，透過機器人、使用者，和使用者的朋友之間的互動，推薦產品。本研究設計一支機器人，處理這個研究缺口，除了推薦產品給目標使用者，也推薦給他們的朋友圈。實驗結果證實這個推薦引擎的績效優於傳統的推薦機制。此外，本研究也提出假說，並且證實使用者的行為強度對於推薦效果產生顯著的影響。尤其是，經常張貼長訊息和獲得更多回應的活躍型使用者，和比較不活躍的使用者比起來，對推薦效果產生更大的影響。

摘要(英)

Although researchers have proposed various recommendation systems based on the inferred interests provided by user-generated content on social networking sites, few researchers have realized that recommendations can take advantage of the characteristics of these social media in the form of interactions among bots, users, and users’ friend circles. This study designed a bot to address this research gap and recommended items to target users as well as their circle of friends. The experimental results confirmed that the recommendation engine outperformed other conventional recommendation mechanisms. Additionally, this paper also posits and confirms that user behavior intensity has a significant impact on recommendation effectiveness. In particular, active users who frequently post long messages and elicit more responses exerted a greater impact on recommendation effectiveness than less active users.

關鍵字(中)

★ 社群網站上的使用者行為
★ 微網誌
★ 互動推薦
★ C2朋友圈

關鍵字(英)

★ User behavior on social networking sites
★ Microblog
★ Interaction recommendation
★ C2 of friends

論文目次

中文摘要 i
ABSTRACT ii
INDEX iii
LIST OF FIGURES iv
LIST OF TABLES v
Chapter 1. Introduction 1
Chapter 2. Literature review 4
2.1. Interest detection from messages posted on SNSs 4
2.2. SNS user behavior perspectives 5
Chapter 3. Interaction recommendation mechanisms and performance evaluation 7
3.1. Plurk platform and Karma 7
3.2. The design of a bot to interact with human users 7
3.3. Constructing the PISL 9
3.4. Conducting recommendation to C2 of users 10
3.5. Performance of the proposed system 12
Chapter 4. The impact of users’ behavior on recommendation effectiveness 15
Chapter 5. Data collection, analysis, and discussion 18
5.1. Analysis of the collected data 18
5.2. Discussion 20
Chapter 6. Implications derived from the research 22
6.1. Implications for academic research 22
6.2. Implications for business practitioners 23
Chapter 7. Conclusion 24
References 25

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指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2017-7-7

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