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研究生:崔嘉祐
研究生(外文):Tsui, Chia-Yu
論文名稱:社群協同合作平台之推薦問題研究-以GitHub為例
論文名稱(外文):A study of recommendations on social collaboration platforms : using GitHub as an example
指導教授:蔡銘峰蔡銘峰引用關係王釧茹
指導教授(外文):Tsai, Ming-FengWang, Chuan-Ju
學位類別:碩士
校院名稱:國立政治大學
系所名稱:資訊科學學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:106
語文別:中文
論文頁數:26
中文關鍵詞:推薦協同過濾基於內容過濾分解機器
外文關鍵詞:RecommendationCollaborative filteringContent-based filteringFactorizaction machine
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本論文提出一種在社群協作平台 GitHub 上的推薦方法,利用社群 協作平台上的資訊於分解機器 ( Factorizaction Machines ,簡稱 FM ) 模 型中。 首先,我們抽取專案的協作關係、專案內的文字與程式碼,當 作是特徵資訊加入模型中訓練,進而以模型的訓練結果去做推薦。我 們利用 GitHub 平台上, 開發者對專案的行為 ( 如,給予的星號、關 注、開分支編輯與貢獻 ) ,去建立開發者對專案感興趣評級,並產生 使用者與專案的關係矩陣來當作我們的學習目標。 藉由這樣的方法, 我們不僅能夠幫助模型收斂還能提升推薦的結果,而這種透過不同 數量的相似特徵方法還可幫助使用者接觸到更多面向的物品。在實驗 中, 不論是加入文字特徵還是程式碼特徵, 相較於傳統的推薦方法協 同過濾,我們在平均精確均值 ( Mean Average Precision, MAP) 、 召回 率 ( Recall ) 與 F1 分數 ( F1 score ) 三個評估下都有較優秀的表現。 最 後,實驗結果顯示,在這種協作開發專案的 GitHub 社群協作平台上, 除了一般文字資訊外,程式碼資訊在推薦上是更有幫助的特徵資訊。
This paper proposes a recommendation approach based on Factorization Machines (FM) for GitHub, a social collaborative platform for program de- velopment. This work first extracts several features related to collaboration relationship and textual information within the project and the codes, and then incorporates the features into the model for training and learning. Lastly, the learned models are utilized for recommendation. This work skillfully uti- lizes the behaviors of developers toward a project, such as the star labeling, watch, fork, and contribution, to establish the degree of interest of a devel- oper has toward a project. Then, the proposed approach follows the con- struction of User-Item matrix for conducting the FM learning process. This approach not only expedites the convergence speed and the accuracy of FM, but it also enables users to explore the objects from different aspects. In the experiments, we compare the proposed approach with the traditional collab- oration filtering methods in terms of Mean Average Precision (MAP), Recall and F1 measures. The experimental results show that the proposed method outperforms the traditional user-based and item-based collaboration filtering methods. Furthermore, the experiment shows that, for social collaboration platform for program development, the incorporation of code feature is of greater enhancement than textual feature in the task of recommendation.
第一章介紹 1
1.1 前言 1
1.2 研究目的 2
第二章相關文獻探討 3
2.1 推薦系統( Recommendation Systems ) 3
2.1.1 協同過濾( Collaborative Filtering ) 3
2.1.2 基於內容過濾( Content-Based Filtering ) 4
2.1.3 混合型演算法 4
2.2 分解機器( Factorization Machines ) 5
第三章研究方法 7
3.1 一般的分解機器( Standard Factorization Machine ) 7
3.2 社群協作平台的推薦框架 7
3.3 基於內容特徵( Content-based Feature ) 9
3.3.1 一般文字的特徵資訊 10
3.3.2 程式碼的特徵資訊 11
第四章實驗結果與討論 13
4.1 實驗設定 13
4.1.1 資料蒐集 13
4.1.2 實驗資料集 14
4.1.3 評估指標 15
4.2 社群協作平台推薦系統 15
4.2.1 基於協同過濾的推薦 16
4.2.2 分解機器加入文字特徵 16
4.2.3 分解機器加入程式碼特徵 17
4.2.4 分解機器加入文字特徵與程式碼特徵 17
4.3 敏感度測試 18
4.3.1 隱向量的維度k 18
4.3.2 模型執行迴圈次數 21
4.4 實驗結果之改善重要性檢測 22
4.5 特徵權重 22
第五章結論 24
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