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
Other Title
Individualized New Product Recommendation System:
A Comparison and Integration of Behavior-Based and Recognition-Based Approaches
A Comparison and Integration of Behavior-Based and Recognition-Based Approaches
Date Issued
2004
Date
2004
Author(s)
DOI
922416H002008
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.
Subjects
Product Recommendation System
Preference Structure Analysis
Hierarchical Bayes
Models
Models
Publisher
臺北市：國立臺灣大學國際企業學系暨研究所
Type
report
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