跨平台推薦系統，基於Facebook使用者特質推薦Instagram熱門帳號

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姓名

李嘉信(Jia-Shin Li) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

跨平台推薦系統，基於Facebook使用者特質推薦Instagram熱門帳號

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至系統瀏覽論文 (2025-6-27以後開放)

摘要(中)

隨著社交媒體在人們生活中扮演的角色越來越重要，吸引了各個商業圈、研究圈投入的大量資金與研究。因此，該如何有效的提高社交媒體向目的客戶群分發相關的資訊及廣告已經變成了一個非常重要的問題。且由於社交媒體的普及化，導致擁有大量知識豐富的用戶參與，因此社交媒體更可以視為一項支持用戶下決策的有力參考因素。甚至於用戶會受到自己的朋友或追蹤者的影響而受到對同類型事物很大程度的喜好度影響。但大多數的社交媒體個性化推薦服務都基於單一平台用戶建模。這可能將會遇到數據短缺和用戶數量不足等問題。在本文中，我們通過彙整兩大社群平台的用戶資訊，分別為 Facebook不公開使用者資訊與 Instagram公開熱門帳號資訊。並建立跨平台推薦模型作為解決方案。傳統推薦方法通常需要對推薦目標有非常多的資訊才會有較好的推薦效果，相反的，當對目標的特質、興趣不太了解的時候，效果就會變差。而本文提出的辦法與傳統方法不一樣的是我們可以克服對目標資訊不足時還可以擁有不錯的推薦效果。本文透過分析兩大目標平台的用戶行為資訊並使用基於內容 (Content base)的方法考量兩者間的特質相關性。並嚴謹的設計實驗且透過號招實際用戶幫助我們證明此方法的有效性。

摘要(英)

Currently, although many recommended system applications are launched, usually these recommended applications are executed on the same platform. These single-platform recommendation systems face two challenges. The first problem is the lack of data that can be referenced and used in the recommendation system. For example, for the Facebook platform, the recommended material can only come from the Facebook community itself, not from Instagram. The second problem is the problem of insufficient number of users. For example, advertisers on Instagram can only send their ads to users on Instagram, not Facebook users. In response to these two problems, this paper proposes a cross-platform recommendation system from Facebook to Instagram. This has two advantages. First, the data of the two platforms can be integrated and complement each other, thereby greatly expanding the source and richness of recommended data. Second, Instagram advertisers can not only send ads to users on the same platform, but to Facebook users with the same preferences. This can help the system expand its customer base and help better target marketing. Finally, we use a series of experiments to prove the effectiveness of the entire method. Experimental results show that this method has a good effect on the similarity analysis of Facebook users and Instagram popular accounts, and the recommendation results also highly match the user′s preferences.

關鍵字(中)

★ 推薦
★ 社群媒體
★ Facebook
★ Instagram

關鍵字(英)

★ Recommendation
★ social media
★ Facebook
★ Instagram

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 vii
表目錄 viii
第一章、緒論 1
1-1研究背景 1
1-2研究動機 3
1-2-1 數據短缺。 3
1-2-2 用戶數量不足。 3
1-3 研究目的 3
第二章、文獻探討 5
2-1-1 基於社群媒體的產品推薦 5
2-1-2 基於社群媒體的意見領袖推薦 6
2-1-3 基於社群媒體的旅遊路線推薦 7
2-1-4 基於社群媒體的新聞廣告推薦 8
2-1-5 基於社群媒體的Hashtags推薦 8
2-1-6 基於社群媒體的朋友推薦 9
2-1-7 基於社群媒體的電影推薦 10
2-1-8基於社群媒體的推薦系統總結 10
2-2 社交媒體的常用推薦技術 11
2-2-1 基於內容(Content Based) 12
2-2-2 協同過濾(Collaborative Filtering) 12
2-2-3 混合推薦(Hybrid Recommendation ) 13
第三章、研究架構 15
3-1 研究概述 15
3-2 Preference similarity module 16
3-2-1 文章內文整合 18
3-2-2 文本斷字斷詞處理 19
3-2-3 文本冗言清理 19
3-2-4 取得LDA概率分布 19
3-2-5 計算LDA 相似度，取得 Pre(Fi,Ij) 20
3-3 Popularity Calculation module 21
3-3-1 用戶發文(post)的平均響應次數 22
3-3-2 用戶post響應的平均情緒分數 22
3-3-3 用戶Post被點擊“LIKE”的平均數 23
3-3-4 用戶被多少使用者追隨(Follow) 23
3-3-5 用戶被朋友標記Post總數 24
3-3-6 標準化(normalize) 24
3-3-7 取得Pop (Fi, Ij) 總分 25
3-4 Activity Calculation Module 26
3-4-1 用戶發布的post數量 27
3-4-2 用戶最近一個月的post數量 27
3-4-3 用戶最新一次發文在多久以前 28
3-4-4 用戶追蹤了多少人 28
3-4-5 標準化(normalize) 29
3-4-6 取得Act (Fi , Ij) 30
3-5 Picture Similarity Module 31
3-5-1 相似度計算演算法 32
3-5-2 關鍵字提取說明 33
3-5-3 文字向量化說明 34
3-5-4 餘弦相似度計算 35
3-5-4 Pic(Fi, Ij)取得 36
第四章實驗設計 38
4-1 實驗環境 38
4-1-1 Facebook 個人貼文 38
4-1-2 Instagram公開帳號資訊 38
4-1-3系統開發平台 38
4-2 實驗資料蒐集 39
4-2-1 Facebook使用者個人資料蒐集 40
4-2-2 20個Instagram熱門公開帳號類型差異性 41
4-2-3 Instagram帳號相關資料蒐集 42
4-2-4 使用者對Instagram帳號喜好度評分表 43
4-3 衡量指標 43
4-4 實驗架構 45
4-5 實驗一 45
4-5-1 實驗一小結 46
4-6 實驗二 47
4-6-1 實驗二小結 48
4-7 實驗三 49
4-7-1 實驗三小結 50
4-8 實驗四 51
4-8-1 實驗四小結 52
4-9 實驗五 53
4-9-1 實驗五小結 54
4-10 實驗總結 55
4-10-1 情況一 55
4-10-2 情況二 56
4-10-3 情況三 57
4-10-4 實驗結論 58
第五章結論 59
5 -1 研究貢獻 59
5 -2 未來研究 59
參考文獻 61
附錄:使用者對20位Instagram帳號喜好度 65

圖目錄
圖 1 系統架構圖 16
圖 2 Pre 流程圖 18
圖 3 分詞精確模式範例 19
圖 4 線性正規化範例 30
圖 5 Picture Similarity Module - 流程圖 32
圖 6 Picture Similarity Module - 演算法 33
圖 7 Picture Similarity Module - 關鍵字提取 34
圖 8 Picture Similarity Module - 關鍵字向量化 35
圖 9 Picture Similarity Module - 餘弦相似度比較 36
圖 10 Picture Similarity Module - Pic(Fi, Ij) 取得 37
圖 11 JupyterLab平台示意圖 39
圖 12 實驗一皮爾森相關係數 46
圖 13 實驗二權重分配 48
圖 14 實驗二皮爾森相關係數 48
圖 15 實驗三權重分配 50
圖 16 實驗三皮爾森相關係數 50
圖 17 實驗四權重分配與結果 52
圖 18 實驗五權重分配與結果 54
圖 19 實驗總結情況一 56
圖 20 實驗總結情況二 57
圖 21 實驗總結情況三 58

表目錄
表格 1 Facebook 使用者相關資料 40
表格 2 Instagram 帳號背景資料 41
表格 3 Instagram 帳號相關資料 42
表格 4 使用者對Instagram帳號喜好度評分表 43
表格 5 皮爾森相關性意義 44

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指導教授

陳彥良(Yan-Liang chen)

審核日期

2020-6-17

推文