臺灣博碩士論文加值系統

隨著科技的進步與電商的蓬勃發展，用戶面對眾多的商品種類，無法藉由瀏覽搜索就得到想要的結果。現今的推薦系統（Recommendation System）廣泛應用於各行各業，可用於預測用戶的購買喜好。傳統的推薦系統只考慮到用戶以往購買過的商品以及用戶間購買商品的關係，而沒有考慮到用戶彼此的社交關係。然而用戶的購買行為往往受到同學、朋友、親戚和同事之影響。因此本論文提出使用深度學習（Deep Learning）的圖形神經網路（Graph Neural Network, GNN）的方法，利用用戶間親疏的社交關係與用戶購買商品的評分，加上購買商品間的關係，預測可能購買的商品，提升推薦商品的準確度。

With the advancement of technology and the vigorous development of e-commerce, users are faced with numerous product categories, and they cannot get the desired results by browsing and searching. The recommendation systems are widely used in various fields and can be used to predict users’ purchasing preferences. The traditional recommendation system only considers the products that users have purchased in the past and the relationship between the purchased products between users, but does not consider the social relations of users. However, users’ purchase behavior is often influenced by their classmates, friends, relatives and colleagues. Therefore, this thesis proposes a Graph Neural Network (GNN) mechanism based on deep learning that can predict the possible purchased products and get high accuracy of the recommended products from the social tie strengths.

摘 要 i
Abstract ii
目錄 iv
表目錄 vi
圖目錄 vii
第一章　緒論 1
1.1研究背景與動機 1
1.2研究目的 8
1.3研究架構與流程 9
第二章　文獻探討 10
2.1推薦系統文獻回顧 10
2.2圖神經網路相關研究 11
2.3深度學習相關研究 12
第三章　基於社交關係的GNN推薦系統 14
3.1模型架構 14
3.2社交關係評分 15
3.3定義相關符號與算式 17
3.3.1查詢層（Query Layer） 18
3.3.2鄰居採樣（Neighbor Sampling） 19
3.3.3關係注意力（Relation Attention） 21
3.3.4評分預測及優化（Rating Prediction and Optimization） 22
第四章　實驗建模與分析 23
4.1參數設定 23
4.2評估指標 MAE、RMSE 24
第五章　結論 28
參考文獻 29

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