運用資料探勘技術於混合式推薦系統預測整合之研究

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Title:	運用資料探勘技術於混合式推薦系統預測整合之研究 Applying Data Mining Techniques to Combine Predictions in Hybrid Recommender Systems
Authors:	范景怡 Fan, Ching-Yi
Contributors:	資訊管理學系碩士在職專班
Keywords:	混合式推薦系統資料探勘線性回歸類神經 Hybrid Recommender System Data Mining Linear Regression Neural Networks
Date:	2013-06
Issue Date:	2013-10-15 13:43:25 (UTC+8)
Abstract:	推薦系統發展至今已產生多種不同的技術，大致上主要可分為：內容式過濾、協同式過濾、人口特徵過濾這三種，但是這些技術在個別使用上，都分別有一些潛在的問題存在。有鑑於此，已有不少的學者，提出改以數種推薦方式混合使用來進行，目的希望能夠截長補短，降低單一方法的缺點，以達到提供更精確的推薦。然而目前大部分被使用的混合方式，大多是根據過去的研究經驗，選擇行之有效的啟發式方法來進行混合推薦，缺乏嚴謹的理論基礎。因此本研究嘗試運用推薦技術的概念，分別以內容式過濾、協同式過濾，以及人口特徵過濾，將這些推薦方法結合資料探勘技術，資料探勘分類技術能夠從龐大的資料中，挖掘出各個項目之間隱藏性的知識與規則，並建立資料屬性與類別之間的關係模型，進而以此關係模型做更有效的預測。考量到不同資料探勘技術所展現出來的成效也許會有差異，因此本研究將同時使用線性回歸與類神經網路兩種演算法，來建立一套新的混合推薦系統預測模型，並比較不同推薦技術之間的成效，找出最佳的混合推薦模型。 Nowadays, the Recommender System has been developed in several different ways for operating. The main techniques are used to develop Recommender System: CB (Content-Based), CF (Collaborative Filtering) and DF (Demographic Filtering). However, each technique has its advantages and limitations. For this reason, many scholars have proposed combine several techniques, intended to reduce the disadvantages of a single method, and achieve more precise recommendation. Currently, the main techniques are used to develop Recommender System, mostly according to the experience of the past research or heuristic method. It lacks of rigorous theoretical foundation. Therefore, this study hopes to use the concepts of CB and CF, plus DF techniques, combining the Data Mining techniques (i.e., Linear Regression, Neural Networks) with the predication. To sum up, it will provide a more accurate prediction than one single technique, and overcome the limitations of each respective potential problem.
Appears in Collections:	[Department of Information Management & Graduate Institute of Information Management] Thesis

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