摘要
本研究聚焦於電子商務領域的蓬勃發展以及個性化推薦系統的重要性。隨著電子商務的快速擴展，企業面臨著從傳統大量生產轉向個性化定制的挑戰。這種轉變要求企業提供符合多樣客戶需求的產品，而推薦系統成為解決大量信息問題的有效途徑。客戶需要處理大量信息以選擇符合其需求的商品，而推薦系統能夠幫助客戶過濾和推薦出最符合需求的商品，提供個性化的購物體驗。

推薦系統的應用對於電子商務至關重要，不僅能夠提高客戶滿意度，還有助於企業提高銷售額。透過推薦系統，企業能更好地了解客戶的喜好和需求，提供更精準的商品推薦，進一步加強客戶忠誠度。因此，推薦系統成為能夠重塑電子商務世界的重要商業工具。大型電商網站已廣泛應用推薦系統，提高客戶購物體驗，並增加銷售額。串流媒體平台如Netflix、YouTube、Spotify等也透過推薦系統提高使用者黏著度。
因此，在寵物食品用品等相關產業蓬勃發展，以及電子商務的技術日趨成熟的背景下，推薦系統在寵物食品用品電商平台領域上的應用，應為一個可進行研究之議題。本研究主要想利用「基於稀疏非負矩陣分解的做法」，以NMF（Non-negative Matrix Factorization）和nsNMF（Nonsmooth Non-negative Matrix Factorization）為例，探討推薦系統的實際應用結果，並分析其中的應用差異。

Abstract
This research focuses on the flourishing development of the e-commerce sector and the significance of personalized recommendation systems. With the rapid expansion of e-commerce, businesses are challenged to shift from traditional mass production to personalized customization. This transformation requires companies to offer products that cater to diverse customer needs, and recommendation systems have become an effective way to address the challenges posed by the abundance of information. Customers often deal with a vast amount of information when selecting products that meet their needs, and recommendation systems assist them in filtering and suggesting items that align with their preferences, providing a personalized shopping experience.
The application of recommendation systems is crucial in the realm of e-commerce, not only enhancing customer satisfaction but also contributing to increased sales. Through recommendation systems, businesses can gain a better understanding of customer preferences and needs, offering more precise product recommendations to strengthen customer loyalty. Therefore, recommendation systems have become a vital business tool capable of reshaping the e-commerce landscape. Major e-commerce websites widely implement recommendation systems to improve customer shopping experiences and boost sales. Streaming platforms such as Netflix, YouTube, Spotify, and others also leverage recommendation systems to enhance user engagement.
Given the thriving development in related industries, such as pet food and supplies, and the increasing maturity of e-commerce technology, the application of recommendation systems in this field becomes a topic worthy of in-depth research. This paper aims to utilize the "approach based on sparse non-negative matrix factorization." Using examples of NMF (Non-negative Matrix Factorization) and nsNMF (Nonsmooth Non-negative Matrix Factorization), we explore the practical application results of recommendation systems and analyze the variations in their applications.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
目錄 vi
圖次 vii
表次 viii
第一章、研究背景、動機及目的 1
第一節、研究背景 1
第二節、研究動機 3
第三節、研究目的 4
第二章、文獻探討 5
第一節、推薦系統 5
第二節、NMF 7
第三節、nsNMF 8
第四節、推薦系統在電子商務上的應用 10
第三章、研究設計及方法 11
第一節、研究方法 11
第二節、研究資料 14
第三節、資料預處理過程 15
第四節、分析客戶行為和產品特性探索 19
第四章、研究成果 31
第一節、資料集說明 31
第二節、實驗說明 34
第五章、研究結論 49
第一節、研究結果 49
第二節、研究限制與未來研究方法 53
第六章、參考文獻 54
附錄 本研究各分群實驗結果 58

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