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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/111161

    Title: 結合網路輿情的電子商務推薦系統之研究 : 以手機產品為例
    Other Titles: A study of e-commerce recommendation systems based on sentiment analysis : a case study of mobile phones
    Authors: 張琇媛;Chang, Hsiu-Yuan
    Contributors: 淡江大學資訊管理學系碩士班
    蕭瑞祥;戴敏育;Shaw, Ruey-Shiang;Day, Min-Yuh
    Keywords: 推薦系統;基於內容過濾;協同過濾;輿情分析;電子商務;Recommendation System;Content-based Filtering;Collaborative Filtering;Sentiment analysis;e-commerce
    Date: 2016
    Issue Date: 2017-08-24 23:45:30 (UTC+8)
    Abstract: 由於資訊的爆炸,已造成資訊的供大於求,使用者須經過不斷的瀏覽及搜尋才能找到所喜歡的商品,因此許多學者開始鑽研資訊過濾的機制,此機制除了解決資訊爆炸的問題外,還能向使用者推薦符合其需求的資訊,幫助使用者能夠過濾並選擇適合自己的商品,而推薦系統就是屬於資訊過濾的一種應用,能夠依據使用者的喜好、需求及興趣,將資訊或商品推薦給使用者,減少使用者在搜尋過程中付出額外時間成本。目前常見的推薦系統形式有內容式過濾、協同式過濾,以及結合上述兩種的混合式,目前混合式推薦已被廣泛應用於電子商務業界。
    Due to the information explosion which causes the overload of information, the users have to continuously browse and search to get the information they preferred. Therefore, many scholars start to study information filtering to reduce the problem of information explosion and help user to select the items based on user’s preferences. Recommendation system is one of the use of the information filtering which can provide information or item based on user’s preferences, requirements and interests more efficiently and precisely. Currently, the recommendation methods can be categorized into the following three types: content-based, collaborative and hybrid recommendation methods. The hybrid recommendation method has been most widely used in the field of e-commerce.
    In order to meet the needs of the user when select products from the recommendation system, this study therefore presented the e-commerce recommendation system with sentiment analysis (ECRS-SA). By using automatic data extraction, opinion extraction, and polarity analysis to collected user’s usage records and comments from the social networks. Then rate the products according to the results of sentiment analysis and give recommendation to users. Finally, we compared ECRS-SA and ECRS-SA without Sentiment Analysis (ECRS) to evaluate user’s need and recommendation accuracy.
    In this paper, we used the survey of satisfaction and the recommendation system evaluated method to analyze user’s usage records. The experiment results show that eighty percent of people satisfy with the ECRS-SA and the F-Measure of the recommendation result is 70.48% higher than the ECRS by 15.75%. The ECRS-SA results is much match to user’s need and the accuracy rate is over ninety percent. And the user’s preference will be impacted by the ECRS-SA.
    Appears in Collections:[資訊管理學系暨研究所] 學位論文

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