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    题名: 結合意見探勘之電影推薦系統的研究
    其它题名: A study of movie recommendation systems with opinion mining
    作者: 陳仕堯;Chen, Shi-Yao
    贡献者: 淡江大學資訊管理學系碩士班
    蕭瑞祥;Shaw, Ruey-Shiang
    关键词: 推薦系統;意見探勘;協同過濾;Movie Recommendation System;Opinion Mining;Collaborative-Filtering
    日期: 2017
    上传时间: 2018-08-03 14:53:50 (UTC+8)
    摘要: 由於在這資訊爆炸的時代中,網路上充斥著非常多的資訊,使用者需要瀏覽非常多的評論進行比較,最後再做出相關決策,因此許多學者開始研究相關資訊過濾的機制,而推薦系統就是屬於資訊過濾的一種,推薦系統可從大量的資訊中,可以依據使用者的年齡、性別或是需求,系統會記錄相關資訊並進行過濾,進而將資訊或是商品推薦給使用者,目前常見的推薦系統有內容過濾式推薦(Content-Based Filtering)、協同過濾式(Collaborative-Filtering)以及混合過濾式(Hybrid-Based Filtering)。
    目前一般電影網站僅提供電影整體星等評價、預告片、導演等相關電影資訊,而民眾在觀賞電影前除了會參考整體的星等評價外,還會參考網路上他人的評論,才會決定是否觀賞電影,為了減少使用者在搜尋過程中付出額外時間成本,本研究提出一個結合意見探勘的電影推薦系統(以下簡稱結合意見探勘型),於系統背景中,蒐集使用者記錄,透過自動化、意見單元定義擷取等步驟收集IMDb網友的評論,對電影各個面向進行網路輿情分析,給予每部電影權重分數並進行推薦,最後將結合意見探勘型與傳統型電影推薦系統(以下簡稱一般型)兩者作比較,並探討使用者之使用意願與推薦準確率來評估本研究之成果。
    本研究利用問卷調查、收集使用者紀錄進行推薦系統評估並得知近八成民眾滿意結合意見探勘型,且從推薦系統評估的結果中得知F-Measure為68.06%比起一般型高出7.54%,其推薦準確率達到82.84%,可以發現本研究提出的結合意見探勘型之推薦結果較符合使用者心中意願。
    As the shift toward the big data continues, the internet is continued to be filled with amounts of information. Users are required to browse through these unfiltered data to make relevant decisions. So many researchers begin to study the classification of information filtering, such as a recommendation system. The system can record information and filter based on the age, gender, or needs of the user from a large amount of information . The more specific the criteria, the more accurate the results will be. There are currently three widely used recommendation systems, Content-Based Filtering, Collaborative Filtering, and the Hybrid-based.
    In the past, film reviewers only provided an overall score on the films reviewed. Now, consumers not only take film score into consideration, but also comments and opinions submitted by other users online. Therefore, this study proposes a collaborative filtering recommendation system,"A film recommendation system that incorporates consumer opinions(hereinafter referred to as the traditional method with opinion mining), and explores the details of the film recommended system. Then for each movie element analysis and give each film weight score and recommended. Finally, let traditional method with opinion mining and traditional film recommendation system (hereinafter referred to as the traditional method) for comparison to make users more in line with the user''s expectations.
    From the results of our study, we found that by incorporating opinion mining, the F-Measure was 68.06%, which was 7.54% higher than that of the standard method. Compared with the standard film recommendation system, the system incorporating opinion mining is more in line with the opinions of the users.
    显示于类别:[資訊管理學系暨研究所] 學位論文

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