English  |  正體中文  |  简体中文  |  Items with full text/Total items : 62805/95882 (66%)
Visitors : 3882890      Online Users : 297
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/74577

    Title: 推薦系統的架構與實作
    Other Titles: The framework of recommender system and implementation
    Authors: 紀政宏;Chi, Zheng-Hong
    Contributors: 淡江大學資訊工程學系碩士班
    蔣定安;Chiang, Ding-An
    Keywords: 推薦系統;以內容為導向推薦系統;協同過濾推薦系統;混合式推薦系統;Recommender System;Content-based Recommender System;Collaborative Filtering Recommender System;Hybrid Recommender System
    Date: 2011
    Issue Date: 2011-12-28 18:56:22 (UTC+8)
    Abstract: 隨著網路科技的快速崛起,使得資訊發展所帶來的便利在生活上無所不再,而資料成長速度更是一日千里,若想在浩瀚的資料海中,尋找實際有用的資訊則相對少量,正是企業與學者主要關注的議題。為了增加企業的利益,業者必須充分蒐集與客戶相關的資訊,來經營與客戶之間的關係,以資料探勘技術來了解客戶喜好的變化,甚至利用機器學習來發展與客戶互動的智慧型商業平台,而近年來使用推薦技術應用在客戶與產品之間的行銷與決策。以電子商務而言,用於推薦分析主要是根據客戶的評價資料與交易資料,在推薦領域上,評價資料就是客戶直接給產品評價的顯性評比,而交易資料則像是客戶在網路上購買或瀏覽商品時所遺留下來的瀏覽率與購買頻率,稱為隱性評比。而本論文希望可以提出一個協同過濾的推薦架構,能應用在評價資料與交易資料上的推薦,透過客戶資料在推薦架構的應用能用在教學研究,以簡單易學的架構與流程,透過實例運算與實際資料集的測試讓初學者能快速產生連結;除此之外,本架構還可用在資料測試並讓用戶可將推薦結果與其他推薦系統作比較,讓用戶評估並選擇最佳的推薦效果。
    Due to the rapid development of Internet, it brings humans not only more convenient in life but also makes data growth up rapidly. People face with the amount of information available and expect to use it efficiently. In order to increase the benefit, businessman must fully collect information related to customer and manage customer relationship. Recommender system has been applied for cross-selling or increasing profit between customers and products in recent years. It has been become popular for making useful recommendation to online e-commerce sites. In this paper, we mainly use the rating data or transaction data applied in recommender system. The rating data from item rated directly by customers is called explicit rating. Also, the transaction data, such as web browsing clicks, purchased records and so on, is called implicit rating. We expect to provide a framework of recommender system that employs collaborative filtering to derive recommendations. The framework can be used for teaching and research. Moreover, the process can offer a practical computation instances and dataset for freshman to learn and map easily. In this framework users can utilize data for measuring, or find out proper recommendation results through comparing with other techniques.
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Thesis

    Files in This Item:

    File SizeFormat

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback