資料採礦於臉書商業模式推薦機制之研究

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Title:	資料採礦於臉書商業模式推薦機制之研究
Other Titles:	The study of data mining implements on a recommendation mechanism for Facebook business model
Authors:	何欣容;He, Sin-Rong
Contributors:	淡江大學管理科學學系碩士班廖述賢;Liao, Shu-Hsien
Keywords:	臉書 Facebook;虛擬社群;加值服務;推薦機制;資料採礦;Facebook;Virtual Community;Recommendation Mechanism;Value-added service;Recommendation System;data mining
Date:	2014
Issue Date:	2015-05-04 09:49:28 (UTC+8)
Abstract:	隨著「無線化」與「行動化」的連網技術日趨成熟，網際網路可以在不受時空的限制下使用，更加速了資訊產業的發展。各式各樣的虛擬商品也更被廣為使用、買賣和散布，人們在虛擬世界中進行互動的需求與商品的交易，是相輔相成的，未來數年應該還能帶來巨大的利潤與成長(王維聰、張文鴻, 2009)。故本研究欲以臉書Facebook (FB)的使用者為主體，以資料採礦為方法，歸納使用者接受資訊及購物及加值服務偏好之習慣，探討在社群中，FB使用者、FB社群工具、廠商三者之間的相互關係。本研究以839份使用者為樣本，透過集群分析(Clustering analysis)研究結果將分為三個集群，並且運用關聯法則(Association rules )挖掘出有用的資訊和知識且找出相似性的消費特徵，當使用者在FB平台使用工具，利用促銷活動來推薦適合的產品或資訊給予使用者，進而提供企業廠商選擇適當的促銷活動和廣告，以及對使用者於未來FB社群網路平台之加值服務偏好行為，來建議FB社群網站商業模式規劃與操作取向之參考。 As the technology of “wireless” and “mobility” of networking grows mature, the use of internet can be utilized without the constraint of time and space, which boosts the development of information technology nowadays. A big variety of virtual commodities are used, marketed, and spread in a wider extent. The needs of interaction with people are complementary with the trading of goods in the virtual world. Therefore, a remarkable profit and growth in this industry still can be expected in a couple of years ( Wang & Chang , 2009). This study focuses on the analysis of Facebook users to conclude the habits of their information sourcing and shopping preference with a further attempt to discuss the relationship between Facebook users, Facebook networking functions and the business operators in the social community. This study adopts 839 users as samples, dividing the research result into three clusters while Association rules are applied to bring out more useful information and knowledge for the finding of smiliar traits and qualities of consumers'' habits. As a consequence, this study result can be taken as a reference for the industries or companies to recommend the best products or information to the users through the promotion activities, as well as a key factor for their decision on the selection of product promotion, advertising, and timing with a reference to the functions of Facebook platform.
Appears in Collections:	[Department of Management Sciences] Thesis

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