政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/59303

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/59303

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政大典藏 > College of Commerce > Department of MIS > Theses > Item 140.119/59303

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/59303

Title:	改良式協同過濾推薦系統之架構與評估 A framework and evaluation of recommendation system using modified collaborative filtering method
Authors:	張玉佩
Contributors:	李有仁張玉佩
Keywords:	推薦系統協同過濾資料稀疏性冷開始
Date:	2012
Issue Date:	2013-09-02 16:02:19 (UTC+8)
Abstract:	協同過濾是電子商務中最常被使用也是最成功的推薦技術，但隨著電子商務的發展，網站使用者與商品數也迅速成長，使得使用者相關資料稀疏(Data sparsity)而嚴重影響推薦品質。對於新使用者與新商品，協同過濾也無法提供準確的推薦。為改善以上問題，本研究使用Lemire與Maclachlan (2005)所提出的Slope One演算架構及資料探勘方法中的單純貝式分類器(Naïve bayes classifier)來解決資料稀疏性和冷開始(Cold-start)問題。同時，考量到運算成本，將推薦系統架構分為離線預處理階段和線上預測階段，以避免當使用者數目和商品越來越大時運算成本超過實際可接受程度。本研究採用MovieLens資料庫的資料集，包含943位使用者與1,682部電影，共10萬筆評比資料，評比分數範圍從1到5分，其中每位使用者至少評比20部以上電影。實驗評估方法則採用平均絕對誤差(MAE)來計算本研究的推薦系統對消費者喜好預測的準確度。本研究希望所提出的個人化推薦系統能改善傳統協同過濾推薦系統的推薦品質，減少資料稀疏所造成的推薦誤差，更準確的推薦使用者感興趣的物品，以幫助使用者更有效率的進行線上消費，提高顧客滿意度與忠誠度，也提升電子商務網站營業效益。
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Description:	碩士國立政治大學資訊管理研究所 99356035 101
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0993560351
Data Type:	thesis
Appears in Collections:	[Department of MIS] Theses

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