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研究生:張伯新
研究生(外文):Zhang, Bo-Sin
論文名稱:基於異質性資訊網路表示法學習之電子商務推薦系統
論文名稱(外文):E-commerce Recommendation Systems Based on Heterogeneous Information Network Embedding
指導教授:蔡銘峰蔡銘峰引用關係
指導教授(外文):Tsai, Ming-Feng
口試委員:王釧茹蘇家玉
口試委員(外文):Wang, Chuan-JuSu, Chia-Yu
口試日期:2018-07-05
學位類別:碩士
校院名稱:國立政治大學
系所名稱:資訊科學系
學門:工程學門
學類:電資工程學類
論文出版年:2018
畢業學年度:106
語文別:中文
論文頁數:31
中文關鍵詞:網路表示法推薦系統特徵值學習類神經網路
外文關鍵詞:Network embeddingRecommendation systemsFeature learningNeural network
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近年來由於龐大的資料量,電子商務的商品推薦變成一項具有挑戰性的工作。因此,我們使用了異質性資訊網路表示法學習(Heterogeneous Information Network Embedding),能夠將網路上不同類型的節點及之間的關係投影到低維度向量空間,以進行電子商務相關的產品推薦工作。本論文提出了一個基於異質性資訊網路表示法學習的電子商務推薦系統,能夠更有效地整合額外資訊(Meta Information)。首先,我們萃取標題的詞彙並與商品鏈結,再加入使用者過去的歷史紀錄轉換成異質性資訊網路。從這個網路中,我們可以使用各式各樣的網路表示法學習方法訓練,並在同一個向量空間中學習使用者及商品的表示式。除此之外,我們更將學習到的表示法當做特徵值,結合矩陣分解(Matrix Factorization)及排序學習(Learning to Rank)的做法,來達到有效地推薦商品的目的。在實際電商~Amazon~的資料集中,此論文的方法能使推薦的效果有所提升。另外,此方法也能夠有效改善電子商務推薦系統所重視的覆蓋率(Coverage)表現。
In recent years, E-commerce product recommendation has been a challenging task due to its data sparsity and volume. Heterogeneous information network embedding encodes the node information into low-dimensions vector space from different types of nodes and their corresponding relations. In this paper, we propose an E-commerce product recommendation method based on the heterogeneous network embedding. First, we incorporate words from product title as the attributes of the item. Then, we transform words, and user behavior into heterogeneous network for E-commerce. For this network, we use various network embedding methods to learn both user and item representations in the same latent space. Moreover, we integrate the learned embedding as the features into Matrix Factorization and Learning to Rank. The experiment results show that we improve the recommendation quality on Amazon dataset. Also, we demonstrate our model can perform better in terms of coverage, the focus of E-commerce recommendation systems.
致謝 1
中文摘要 2
Abstract 3
第一章 緒論 1
第二章 相關文獻探討 4
2.1 推薦系統 4
2.2 網路表示法學習 5
第三章 研究方法 7
3.1 問題定義 7
3.2 異質性資訊網路表示法學習 8
3.2.1 建圖策略 8
3.2.1 Deepwalk 9
3.2.1 LINE 10
3.2.1 HPE 11
3.2.1 metapath2vec 12
3.3 推薦系統 13
第四章 實驗結果與討論 15
4.1 資料集 15
4.2 實驗設定 16
4.3 評估標準 18
4.4 實驗結果 18
4.4.1 準確率及召回率表現 18
4.4.2 覆蓋率表現 20
4.4.3 建圖策略比較 22
4.4.4 案例分析-網路表示法學習 22
4.4.5 案例分析-推薦系統 25
第五章 結論 27
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