政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/143835

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政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/143835

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/143835

Title:	基於內容偏好圖卷積網路之完全冷啟動推薦演算法 Improving Complete Cold-Start Recommendation via Content-based Preference Graph Convolution Networks
Authors:	王韋勝 Wang, Wei-Sheng
Contributors:	蔡銘峰 Tsai, ‪Ming-Feng 王韋勝 Wang, Wei-Sheng
Keywords:	推薦系統冷起動推薦圖學習表示法 Graph Representation Recommender System Cold-Start Recommendation
Date:	2022
Issue Date:	2023-03-09 18:37:16 (UTC+8)
Abstract:	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.
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Description:	碩士國立政治大學資訊科學系 109753110
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0109753110
Data Type:	thesis
Appears in Collections:	[Department of Computer Science ] Theses

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