DC 欄位 | 值 | 語言 |
dc.contributor.author | 任立中 | zh_TW |
dc.creator | 任立中 | - |
dc.date | 2004 | zh_TW |
dc.date.accessioned | 2006-07-26 | - |
dc.date.accessioned | 2018-06-29T12:20:40Z | - |
dc.date.available | 2006-07-26 | - |
dc.date.available | 2018-06-29T12:20:40Z | - |
dc.date.issued | 2004 | - |
dc.identifier | 922416H002008 | zh_TW |
dc.identifier.uri | http://ntur.lib.ntu.edu.tw//handle/246246/17042 | - |
dc.identifier.uri | http://ntur.lib.ntu.edu.tw/bitstream/246246/17042/1/922416H002008.pdf | - |
dc.description.abstract | 產品推薦系統是落實消費者關係管理的一對
一行銷決策支援系統。在過去的研究中,產品推薦
系統大致可分成兩類,一是合作篩選系統
(collaborative filtering),根據產品間之相關性進行
推薦;另是內容篩選系統(content filtering),根據消
費者自我顯現(self-explicated)的偏好結構進行產品
推薦。這兩種推薦系統皆屬於總合層次,前者需要
大量購買紀錄方能得到較穩定的產品相關結構,後
者則需要集合相似的消費者資料方能得到穩定的
偏好結構,故皆無法充分反映消費者的異質性。此
外,合作篩選系統無法分析消費者選購產品的理由
或偏好結構,故無法進行新產品的推薦,只能就現
有產品進行關連性銷售(cross-selling);傳統的內容
篩選系統無法探討個人偏好結構與人格特質的關
係,故無法進一步應用於對新客戶的產品推薦,只
能針對具有購買紀錄的舊消費者進行產品推薦。有
鑑於此,本研究擬以個人偏好結構(individual
preference structure)為基礎,設計一套同時適用於
新產品與新消費者的產品推薦系統,期使消費者關
係管理之觀念能真正落實於企業之日常操作系統。
本研究根據消費者的主觀認知與客觀行為,分
別建立認知基礎與行為基礎的個人化偏好結構,試
圖比較二者是否具一致性,從而評估以主觀認知為
主的產品推薦系統的有效性。在主觀認知方面,為
降低受訪者的資訊處理負擔,本研究採取自我顯現
偏好,以問卷衡量之;在客觀行為方面,本研究以
相同受訪者的交易紀錄及虛擬產品構成該位消費
者的產品考慮集合,再以層級貝氏Probit 模式估計
個人化的偏好結構。實證結果顯示,認知基礎和行
為基礎的偏好結構不具一致性,這也帶出了以往利
用問卷方式去衡量購買態度,進而預測購買行為的
方式將會發生「適用性」及「預測效度」的問題。 | zh_TW |
dc.description.abstract | Product recommendation system is a
one-to-one marketing decision supporting system,
which put customer relationship management into
practice. In the past research, product
recommendation systems fall into two classes. One is so-called collaborative filtering, which
makes recommendation depending on correlation
structure of all products. The other is known as
content filtering, which makes recommendations
on the basis of consumer’s self-explicated
preference structure for product attributes.
These two systems both are at aggregate level.
The former needs dense purchases history data to
get more stable correlation structure of products;
the latter also needs pooling data sets of
homogeneous customers to get more stable
estimation of preference structure. Besides, for
collaborative filtering provide few reasons for a
recommendation and little information about
preference structure of customers, it lacks the
ability to make entirely new product
recommendation but just make cross-selling
among exiting products. The traditional content
filtering does not analyze the relationship between
individual preference structure and personality, so
it cannot be applied to make recommendation for
entirely new customers who provide no
preference information. Therefore, this paper
will design a product recommendation system
suitable for both new items and new customers at
individual level such that we can put the concept
of customer relationship management into regular
business operation systems.
In marketing research field, conjoint analysis
is a useful method to solve problems about
individual preference structure. Most related
research uses questionnaire-type survey data to
explore customers' subjective recognition but not
actual purchasing behavior. For the develop-
ment and prevalence of database, it is easy to get
purchase history data of every customer and
purchase environment data such as sales
promotion activity and advertisement on the point
of purchase. According to concept of conjoint
analysis, actual purchase behavior could be
decomposed into preference structure and effects
of purchase environment variables through proper
models such as hierarchical Bayes probit model.
Does behavior-based reference structure match
with recognition-based? Which recommenda-
tion system has better predictability? Does it
depend on customer personality or purchase
environment? All these topics will be discussed in this project. | en |
dc.format | application/pdf | zh_TW |
dc.format.extent | 368704 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language | zh-TW | zh_TW |
dc.language.iso | zh_TW | - |
dc.publisher | 臺北市:國立臺灣大學國際企業學系暨研究所 | zh_TW |
dc.rights | 國立臺灣大學國際企業學系暨研究所 | zh_TW |
dc.subject | 新產品推薦系統 | zh_TW |
dc.subject | 偏好結構分析 | zh_TW |
dc.subject | 層級貝氏模式 | zh_TW |
dc.subject | Product Recommendation System | en |
dc.subject | Preference Structure Analysis | en |
dc.subject | Hierarchical Bayes
Models | en |
dc.title | 個人化新產品推薦系統:行為基礎與認知基礎模式之比較與整合 | zh_TW |
dc.title.alternative | Individualized New Product Recommendation System:
A Comparison and Integration of Behavior-Based and Recognition-Based Approaches | en |
dc.type | report | en |
item.fulltext | with fulltext | - |
dc.identifier.uri.fulltext | http://ntur.lib.ntu.edu.tw/bitstream/246246/17042/1/922416H002008.pdf | - |
dc.coverage | 計畫年度:92;起迄日期:2003-08-01/2004-07-31 | zh_TW |
item.openairecristype | http://purl.org/coar/resource_type/c_93fc | - |
item.openairetype | report | - |
item.languageiso639-1 | zh_TW | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.fulltext | with fulltext | - |
顯示於: | 國際企業學系
|