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Title:	以使用者意見提升推薦系統效能之研究 Exploiting User Opinions for Improving Individual Recommendations
Authors:	林金永
Contributors:	蔡銘峰林金永
Keywords:	推薦系統協同過濾文字探勘 Recommender Systems Collaborative Filtering Text Mining Factorization Machines
Date:	2016
Issue Date:	2016-09-02 01:32:47 (UTC+8)
Abstract:	近年來，受惠於網路的盛行及其帶來的便利性，許多網站得以收集到大量的使用者對於商品之評價以及評論，運用這些使用者的回饋資料進行分析，以更精準的進行商業行銷正是當今浪潮。而推薦系統廣泛應用於商業行銷，常用的推薦系統之計算理論，乃依據使用者對商品的評分進行協同式的過濾，以找出合適的產品給予推薦，其理論的基礎是品味相近的消費者應該會喜歡類似的商品，使用者對商品的評分即為此模式所採用的依據，例如：運用User-based Collaborative Filtering ，可以找出與被推薦者的特徵值類似的使用者，並以類似使用者中較高評分的項目作為推薦清單，這種方式能得到相當不錯的推薦結果，且計算的運算量亦不太大。相較之下，以使用者對商品的文字評論作為依據的推薦方法則較為少見，但我們認為文字訊息在推薦系統中亦佔有相當份量的重要性；直覺上，將使用者的評分與其文字評論作結合進行分析，應可更完整呈現該使用者的意向，並進而應能改進推薦系統之推薦效能。在這份論文研究中，我們嘗試結合使用者對商品的評分與文字評論於推薦系統中，並以一份取自TripAdvisor.com的使用者對於飯店評價之資料集進行實驗，透過libFM 建立推薦模型；從實驗結果探討中印證了我們的想法：使用者的文字評論訊息的確能夠用以改進推薦系統之效能。
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Description:	碩士國立政治大學資訊科學系碩士在職專班 101971019
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0101971019
Data Type:	thesis
Appears in Collections:	[Executive Master Program of Computer Science of NCCU] Theses

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