政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/118963

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/118963

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政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/118963

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/118963

Title:	基於異質性資訊網路表示法學習之電子商務推薦系統 E-commerce Recommendation Systems Based on Heterogeneous Information Network Embedding
Authors:	張伯新 Zhang, Bo-Sin
Contributors:	蔡銘峰 Tsai, Ming-Feng 張伯新 Zhang, Bo-Sin
Keywords:	網路表示法推薦系統特徵值學習類神經網路 Network embedding Recommendation systems Feature learning Neural network
Date:	2018
Issue Date:	2018-07-27 12:20:35 (UTC+8)
Abstract:	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.
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Description:	碩士國立政治大學資訊科學系 105753001
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0105753001
Data Type:	thesis
DOI:	10.6814/THE.NCCU.CS.002.2018.B02
Appears in Collections:	[Department of Computer Science ] Theses

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