臺灣博碩士論文加值系統

傳統的混合推薦系統旨在結合協同過濾和內容過濾兩種方式進行推 薦，利用使用者喜好資訊和過去互動過的商品內容資訊來解決資料稀 疏性問題和冷啟動問題。但是，在現實世界中，經常因為產品的性質 讓使用者和產品的互動資料相當稀少或是缺少這些資料，從而導致了 完全冷啟動(Complete Cold Start, CCS)問題，如新聞推薦和新活動推 薦，這是傳統的混合模型無法解決的。
在本文中，我們提出了偏好內容卷積(Preference Content Convolu- tion, PCC)方法，這是一種基於圖卷積網絡(Graph Convolution Net- work, GCN)的圖學習表示方法，該方法可在接受缺失資料的前提下 同時抽取使用者對內容的喜好特徵並結合內容資訊，進而針對冷啟動 問題進行推薦。我們在現實世界中的線上售票服務資料集和圖書資料 集上進行的實驗驗證此方法，其性能優於其他傳統基於內容過濾的方 法和沒有卷積網路的混合模型，為基於卷積網路的模型指出了一個方 向。

Conventional hybrid recommender system aims to address the data spar- sity problem and the cold start problem by leveraging collaborative and content- based filtering, simultaneously leveraging the precious user preference infor- mation and staple item content information. However, in many real-world scenarios, such as news and new event recommendations, the nature of items dictates the complete lack of user-item interaction, leading to the complete cold start (CCS) problem, which traditional hybrid models cannot solve.
In this paper, we propose preference-content convolution (PCC), a con- volutional graph network (GCN) based embedding learning method which jointly captures item content information and user preference over item con- tent. The experiments conducted on the real-world online ticket vending service dataset and news recommendation dataset show that the proposed method significantly outperforms traditional content-based filtering methods and hybrid models without convolution, signifying a promising direction for using the convolution-based model in addressing the CCS problem.

Ch1 緒論 1
1.1 前言 1
1.2 研究目的 2

Ch 2 相關文獻探討 5
2.1 推薦系統(RecommenderSystem) 5
2.1.1 內容過濾(ContentFiltering) 5
2.1.2 協同過濾(CollaborativeFiltering) 6
2.2 冷啟動問題(ColdStartProblem) 9
2.3 圖學習表示法(GraphRepresentation) 10

Ch3 研究方法 12
3.1 問題定義 12
3.2 偏好內容圖卷積框架 13
3.2.1 建圖策略 13
3.2.2 偏好空間建構(Preference Space Construction) 15
3.2.3 內容空間建構(Content Space Construction) 16
3.2.4 混合空間建構(Hybrid Space Construction) 18

Ch4 實驗結果與討論 21
4.1 資料集 21
4.1.1 資料前處理 22
4.2 比較基準模型 22
4.3 實驗設定與評估標準 23
4.3.1 實驗設定 23
4.3.2 評估標準 23
4.4 實驗結果 25
4.4.1 完全冷啟動推薦 25
4.4.2 商品對商品推薦(Item to Item Recommendation) 26
4.5 實例分析 27

Ch5 結論 30
參考文獻 31

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