Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/101253


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:[資訊科學系碩士在職專班] 學位論文

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