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論文中文名稱:基於矩陣分解於社群網路中APP之推薦 [以論文名稱查詢館藏系統]
論文英文名稱:App Recommendation Based on Matrix Factorization in Community Networks [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:管理學院
系所名稱:資訊與財金管理系碩士班
畢業學年度:103
畢業學期:第二學期
中文姓名:蘇景宸
英文姓名:Ching-Chen Su
研究生學號:102938003
學位類別:碩士
語文別:中文
口試日期:2015/07/08
指導教授中文名:翁頌舜
指導教授英文名:Sung-Shun Weng
口試委員中文名:林榮禾;楊欣哲;吳瑞堯
中文關鍵詞:交錯最小平方法推薦系統矩陣分解
英文關鍵詞:Alternative Least SquaresRecommendation SystemsMatrix Factorization
論文中文摘要:近年來社交網路與推薦的議題仍持續受到關注,隨著Facebook、Twitter、Google+等社交網路盛行,透過社交網路資訊的協同過濾演算法,也被證明可以利用社交網路的資訊提高推薦準確度以及紓解資料稀疏性等問題。
本研究推薦方法是透過社群網路,將使用者本身以及社群好友對於APP項目之評分進行蒐集,再依據使用者與好友評分項目找出使用者之間共同喜好,透過相似程度,找出具有相似性較高的好友圈做為資料運算來源,透過矩陣分解中的交錯最小平方法來預測評分予以推薦給目標使用者。
透過相關驗證,證明本系統推薦結果是具有準確性的,在問卷結果顯示予以推薦的項目符合使用者需求,並且對推薦項目是感興趣的,透過具有相似性喜好的好友們予以推薦APP也是具有相當的重要性。
論文英文摘要:The issue of Social Networking and Recommendation has been continuously discussed in recent years. With the prevalence of Facebook、Twitter、Google Plus and other social networks, it has been shown that the collaborative filtering approach can improve the recommendation accuracy and relieve the sparsity problem by using the information obtained through social networks.
This study proposes a recommendation method applying to the community networks. The first step is to collect the rating of users and community friends for the APP items. Second, we find common preferences (similarity) among users through the collection of information, and define the similar friends with higher score as data sources. Third, by using alternating least squares, the prediction score is recommended to the target users.
The experimental results show that the outcome recommended by using the approaches of similar friends and matrix factorization is accurate. Moreover, the questionnaire results show that the recommended ones are accordant with user requirements, and users are interested in the recommended outcomes.
論文目次:摘 要 2
ABSTRACT 3
誌 謝 4
目 錄 I
圖目錄 III
表目錄 IV
第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 4
1.3論文架構 5
第二章 文獻探討 6
2.1網路服務 6
2.1.1社交網路服務(Social Network Service) 6
2.1.2社群網路(Community Networks) 7
2.1.3社群網路的由來 8
2.2 信任 9
2.2.1 信任的定義 9
2.2.2信任圈推論(Trust Circle Inference) 10
2.3推薦系統(RECOMMENDER SYSTEM) 10
2.3.1人口統計過濾法(Demographic Filtering) 11
2.3.2內容導向推薦(Content Filtering Approach) 11
2.3.3協同過濾推薦(Collaborative Filtering Approach) 12
2.3.混合式推薦( Hybrid-Based Approach ) 14
2.4基礎矩陣分解模型(BASIC MATRIX FACTORIZATION MODEL) 15
2.4.1隨機梯度遞減法(Stochastic Gradient Descent) 16
2.4.2交錯最小平方法(Alternating Least Square, ALS) 16
第三章 研究方法 18
3.1系統架構 18
3.2 APP推薦流程 20
3.3預測模組 22
3.3.1資料蒐集 22
3.3.2好友相似度 23
3.3.3結合好友圈與矩陣分解 (matrix factorization) 26
3.4推薦模組 27
第四章 實驗設計與結果 28
4.1系統開發環境與測試資料來源 28
4.2評估指標 31
4.3實驗設計 32
4.3.1實驗說明 32
4.3.2實驗資料 32
4.3.3 T值調整好友圈 33
4.3.4矩陣分解預測結果 34
4.3.4推薦結果 35
4.4APP推薦結果評估 38
第五章 結論 43
5.1研究結論與貢獻 43
5.2管理意涵 44
5.3研究限制與未來展望 45
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論文全文使用權限:同意授權於2019-07-29起公開