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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]
英文姓名:Ching-Chen Su
指導教授英文名:Sung-Shun Weng
英文關鍵詞:Alternative Least SquaresRecommendation SystemsMatrix Factorization
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
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
2.3.1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
2.4.1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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