Journal of Information Technology and Applications
Vol. 2, No. 4, pp. 189-195, 2008
A New Learning Relationship and Interaction Based Recommender System
Business Model
Du-Shiau Tsai*
Department of Information Management, Hsiuping Institute of Technology
11, Gongye Rd., Dali City, Taichung County 412, Taiwan (R.O.C.)
[email protected]
Meng-Yu Lin
Tungshih Elementary School
No.1, 5th Side St., Dongshih Township, Taichung County 423, Taiwan (R.O.C.)
Abstract
Information overload forces online consumers to spend time and efforts searching for the desired products.
To save time and decrease search cost, many online shopping websites have developed recommender systems. In
recent years, many researchers have endeavored to develop hybrid recommender systems to improve the original
systems. However, still quite a few consumers are reluctant to make purchases on online shopping websites,
which results from different types of perceived risks. The study believes that two objective guidelines, (a) to
build a learning relationship with consumers and (b) to know how to interact with consumers, will increase the
possibility to succeed in concluding an online transaction for a recommender system. Above all, the study adopts
the ideas of the E-K-B Model and George Miller’s information processing theory to establish the learning
framework by which the study conducts a business value chain for achieving the above guidelines. Moreover, by
using Seetoo’s strategic matrix analysis, a business model for recommender systems is developed. Finally, the
study selects one successful online music website to show the feasibility of the proposed business model.
KEY WORDS: recommender system, learning relationship, strategic matrix analysis
the two categories of recommender systems still have
their own weaknesses.
Therefore, to improve the two traditional recommender systems, many researchers, by using different recommendation techniques, have developed
hybrid collaborative filtering methods (Ansari et al.,
2000; Burke, 2002; Popescul et al., 2001; Weng &
Liu, 2004). Despite the fact that the hybrid recommender systems do improve the two traditional recommender systems, many consumers are reluctant
to make purchases on online shopping websites
(Mahmood, Bagchi, & Ford, 2004). According to
some researchers, the reluctance to purchase online
results from different types of perceived risks (Gupta
et al., 2004; Mahmood et al., 2004; P. –L. Chen,
2002). Some of those researchers do attempt to reduce the extent of perceived risks by providing service with added value such as virtual reality (Gupta et
al., 2004) and applying the concept of e-Clerks to
online shopping websites (P. –L. Chen, 2002). By
doing so, the websites are given more opportunities
to interact with their consumers. But, the study finds
that the interaction most of the websites have with
their consumers concentrates mainly on the pursuit of
accuracy and efficiency of making recommendations
and lightly on the increasing probability of concluding a transaction.
Therefore, the study proposes that interaction is
1. Introduction
Online shopping is inconvenient because online
consumers are confronted with information overload.
This trend compels online consumers to spend much
more time and efforts searching for the desired products. Therefore, to help online consumers save more
time and search efforts, many online shopping websites have developed different recommender systems
that have become fundamental applications in electronic commerce (Burke, 2002) and have been increasingly popular and globally successful on the
Internet (Ansari, Essegaier, & Kohli, 2000; Sarwar,
Karypis, Konstan, & Riedl, 2000).
Briefly, recommender systems are used to filter
out what online consumers do not want and recommend them what they need. Search engines are an
example of recommender systems (Ansari et al.,
2000). But, different from search engines, recommender systems follow ―the criteria of individualized
and interesting and useful‖ (Burke, 2002). This ―criteria‖ explain that recommender systems are creating
a customized or personalized environment through
the live interaction with customers (Sarwar et al.,
2000). Traditionally, recommender systems are divided into two categories – content-based filtering
recommender systems and collaborative filtering recommender systems (Popescul, Ungar, Pennock, &
Lawrence, 2001). In spite of their respective features,
* Corresponding author.
189
Journal of Information Technology and Applications
Vol. 2, No. 4, pp. 189-195, 2008
built on a learning relationship. In a learning relationship, a clerk and a consumer learn about each
other while obtaining some extent of knowledge that
relates to one particular transaction. If the concept of
the learning relationship is applied to the study, it
indicates that a recommender system should play the
role of the clerk and needs to know how to interact
with its consumers, otherwise concluding a transaction is impossible. Moreover a recommender system
should assist a consumer in making decisions through
the entire transaction Therefore, the study believes
that if a recommender system builds a learning relationship with its consumer, it knows how to interact
with its consumers, and then increases its possibility
to succeed in concluding an online transaction.
There are two purposes in the study. First, apply
the concept of the learning relationship from Pine II,
Peppers and Rogers (1995) into the interaction between recommender systems and consumers to establish a learning framework. Then, a business value
chain is proposed based on the learning framework.
Second, the study develops a business model for a
recommender system (BMRS) according to the business value chain. Specifically, in order to establish
the learning framework, the study adopts the ideas of
E-K-B Model (1986) and George Miller’s information processing theory (Carbonell, 2004). Through
the E-K-B Model, a recommender system identifies
consumer needs. In the process of decoding the information processing theory, the system knows how
to satisfy consumer needs. Once the business value
chain is established, the study uses Seetoo’s strategic
matrix analysis (2001) to develop the BMRS. The
study selects one successful online music website to
examine the feasibility of the proposed model. The
final product of the study is the BMRS whose purpose is to achieve the following anticipated results –
(a) The proposed model can be used to develop a
recommender system (b) The proposed model will
explain the key success factors of a recommender
system.
Firstly, the study introduces the background of
recommender systems, some of their problems, and
the motivation of the study. Secondly, the study reviews the relevant literature of recommender systems,
research on recommender systems, the learning relationship, the E-K-B Model, and the information
processing theory. Thirdly, the study presents the
proposed business value chain and BMRS. Finally,
the study analyzes the proposed model with one online music websites and further discusses the conclusion.
demographics of the consumer, or the prediction of
the consumer’s future buying behavior from his past
buying behavior. Other researchers, Ansari et al.
(2000) posit that based on a consumer’s expressed
preferences among alternative products, preferences
for product attributes, preferences from other consumers, expert judgments, and individual characteristics that may predict preferences. Generally, a content-based filtering system recommends products
based on the correlation between the content of
products and a consumer’s preferences. However,
content-based recommender systems have their own
limitations, such as machine parsable problem (Weng
& Liu, 2004), quality problem (Herlocker, Konstan,
& Riedl, 2000), and no recommendations for no
preference information (Ansari et al., 2000; Popescul
at el., 2001).
In recent years, to reduce the limitations from
content-based filtering recommender systems, many
researchers have developed collaborative filtering
recommender systems. Collaborative filtering recommender systems help people make choices based
on the opinions of other people. Besides, Herlocker et
al. (2000) explain that collaborative filtering systems
have many significant advantages over content-based
filtering systems, but collaborative filtering recommender systems still have their own limitations, such
as unique consumer problem (Popescul at el., 2001),
mass product comparison problem (Weng & Liu,
2004), and no reason for recommendations (Ansari et
al., 2000).
Therefore, different hybrid recommender systems have been designed to reduce the limitations
from either content-based recommender systems or
collaborative ones and to bring other advantages.
Judging from the anticipated results of the previous
studies, most of the researchers have placed their
emphasis on the interaction aimed at technical skills
to improve the traditional recommender systems, but
quite a few consumers still end up purchasing offline
(Mahmood et al., 2004), which results from different
types of perceived risks (Gupta et al., 2004; Mahmood et al., 2004; P. –L. Chen, 2002). Some of those
researchers do attempt to reduce the extent of perceived risks by providing service with added value
such as virtual reality (Gupta et al., 2004) and applying the concept of e-Clerks to online shopping websites (P. –L. Chen, 2002). However, most of them
have been in complete ignorance of the most important thing – the interaction aimed at increasing the
probability of concluding a transaction.
2. Literature Review
2.1 Recommender Systems
2.2 Learning Relationship, The E-K-B
Model, and Information Processing
Theory
Schafer, Konstan, and Riedl (1999) propose that
recommender systems suggest products to their consumers on the basis of the top overall sellers, the
According to Pine II, Peppers, and Rogers
(1995), a learning relationship is defined as ―an ongoing connection that becomes smarter as the two
190
Journal of Information Technology and Applications
Vol. 2, No. 4, pp. 189-195, 2008
interact with each other, collaborating to meet the
consumer’s needs over time‖ (p. 103). In the process
of the development of the learning relationship, a
consumer keeps teaching a recommender system
about his preferences and needs. In this case, the
consumer’s satisfaction and loyalty can thus be increased, and the increase in the visiting frequency of
the consumer can further create more transaction opportunities and benefit the online shopping website
(Lee, Liu, & Lu, 2002). Accordingly, from the perspectives from Pine II et al. and Lee et al., the study
proposes that a recommender system should build a
learning relationship in which the recommender system and its consumer interact with each other.
The study chooses the E-K-B Model (1986) as
the consumer decision-making process in the learning
framework. The decision-making process includes
the five stages – problem recognition, information
search, alternative evaluation, purchase decision, and
post-purchase support. Butler and Peppar (1998) advocate that the consumer decision-making process
enables marketers to explain and predict consumer
behavior, and thereby provides a basis for marketing
decisions.
In fact, there are many theories of learning but
the study adopts the information processing theory
(Carbonell, 2004) as the other element in the learning
framework. The theory holds that learning is a
change in knowledge stored in memory. It is a learning process from attention, encoding, to retrieval.
Specifically, people select information (Attention),
translate information (Encoding), and recall that information (Retrieval) during the learning process.
unit is derived in the format (the stage of the decision-making process, a recommender system or a
consumer, the step of the learning process). Take an
example. The first unit in Figure 2, advertising mechanism, is derived from (R,C,A). R indicates Problem recognition – the first stage of the consumer decision-making process, C indicates Consumer, and A
indicates Attention – one of the steps in the Information Processing.
Figure 1: The Learning Framework
3. The Proposed Business Value Chain
and BMRS
Since the study attempts to answer the question
how a recommender system builds a learning relationship and interacts with its consumers, case study
research method is conducted. The case is CDUniverse. Established in 1996, CDUniverse offers music,
movie, and video game. CDUniverse provides approximately 373 categories of music. Its functions
include Album Advisor, Buyer’s Guides, Top 40,
Listen to Samples, and Customer Reviews.
Through the interworking between the E-K-B
Model and the information processing theory, the
learning framework is established in Figure 1.
Through the learning process, a recommender system
and a consumer learn about their mutual needs in
each stage of the consumer decision-making process.
Each unit of the business value chain is apparently constructed according to the flow of the learning framework. With the development of the six strategic types and the unfolding of business value chain,
the study obtains the BMRS in Figure 2. The index
column in Figure 2 is given to illustrate where the
Figure 2: The BMRS
Some mechanism is further explained as follows:
The purpose of the multiple sensor mechanism is to
log consumer behaviors for the inferring mechanism
191
Journal of Information Technology and Applications
Vol. 2, No. 4, pp. 189-195, 2008
 Assisted by full text search and related links, the
Inferring mechanism works, based on the logs of
the consumer’s input in the search field. Specifically, full text search is supported by the search
options that are used to find more accurate products. The search options that CDUniverse provides include search for Artist, search for Title,
search for Song, search for Soundtrack, and
search for Label. Furthermore, when a consumer
searches for albums in one of the searching options, the mechanism automatically searches for
albums in other searching options as well and
display the result in a section called Related
Links.
 In the form of Suggested Artists, the inferring
mechanism works based on logs of the consumer’s history of navigated hyperlinks.
 In the form of Customers Who Bought This Also
Bought (CWBB for short) and Current Top Seller,
the inferring mechanism works based on the consumer’s past buying history.
List of inferred products: CDUniverse displays the
list of inferred products in the following styles.
 Displayed in a page-by-page style – (a) Search
results of the Multiple Artist Match including top
matches and matches (b) Search results for Title
(c) Search results for Song (d) Search results for
Soundtrack (e) Search results of The Multiple
Label Match including top matches and matches
in a page by page viewing style.
 Displayed in a section which can be found on the
left or right hand side of the website – (a) Results
of Related Links (b) Results of Suggested Artists
and (c) Results of Current Top Seller.
 Displayed with limited numbers of products –
Result of CWBB.
Weight of list of inferred products: CDUniverse
provides the two approaches for consumers to weigh
the list of inferred products in order to find the albums they need as soon as possible.
 Sort the list according to Title, Top Sales, Top
sales all time, Price: Low to high, price: high to
low, Rating, release year, street date: new to old.
Street date: old to new
 View the list with constraints such as Everything( no constraints), Imports, Ships Immediately,
Top Sellers, $5-$10, $10-$20, $20-$30, $30-40,
$50+, top sellers, future releases, recent releases
and just announced.
List of confirmed products: CDUniverse receives
the list of confirmed products as a consumer clicks
the hyperlink that he chooses. But, the consumer can
only choose one of the confirmed products that he
wants to confirm.
Search engine: CDUniverse supports the search engine, described as follows:
 Search the exact name of the product in the search
field by using full text search with the search op-
with several sensors. The logs, for example, consist
of inputs of the search box, the staying time of each
page, and the click times of each hyperlink. The purpose of the inferring mechanism is to identify the
products consumers intend to purchase or search for.
For example, if the search box is equipped with the
multiple sensor mechanism, it then can correct a user’s typos, such as Google. Furthermore, the study
assumes that the inputs of the search engine should
provide enough information so as to find the exact
products such as the name or the classification of the
product. With the BMRS, the study selects the learning strategic points that it intends to analyze. The
strategic points the study selects are listed and the
reasons are explained as follows:
 (A21→B21) – The goal of a recommender system
is to provide consumers with the most appropriate
products.
 (A1→A2→, …, →A20→A21) – To provide
consumers with the most appropriate products, the
qualities of the products provided by a recommender system should be guaranteed, just as indicated from A1 to A20. Moreover, any subset of
A1 to A20 following the transaction flow is acceptable because the consumer can choose the
most natural way to complete each transaction.
 (A4→B4), (A8→B8), (A12→B12), (A16→B16),
(A20→B20) – What a recommender system
demonstrates must satisfies consumer needs. Most
importantly, what a recommender system should
provide is a personalized environment.
 (F2→A2), (F3 →A3), (F7 →A7), (F11→A11),
(F15→A15), (F19→A19) – One of the competitive advantages of a recommender system is
the information systems.
 (E2→A2), (E3→A3→A4), (E7→A7→A8), (E11
→A11→A12), (E15→A15→A16), (E19→A19→
A20) – In order to provide consumers a stable
shopping environment and acceptable response
time in each of the information systems, the
number of duplicate servers and the location of
which should be assigned according to the number of consumers in each area.
4. Case Study
The study uses Air Supply as an example to examine the BMRS with CDUniverse’s business value
chain, selected units of the business value chain are
explained as follows.
Multiple sensor mechanism: CDUniverse supports
this mechanism, described as follows.
 Log the consumer’s inputs in the search field.
 Log the consumer’s history of navigated hyperlinks.
 Log the consumer’s past buying history.
Inferring mechanism: CDUniverse supports the
inferring mechanism, described as follows.
192
Journal of Information Technology and Applications
Vol. 2, No. 4, pp. 189-195, 2008
tions such as search for Artist, for Title, for Song,
for Soundtrack, and for Label.
 Search the related information as a consumer
clicks the hyperlink or the category of music.
Evaluating systems: CDUniverse supports the evaluating systems but without dynamical suggestion
function.
Final order list: CDUniverse provides the form with
products on the order, shipping options and payment
options. Moreover, consumers have to choose their
own shipping options and payment options in this
form.
Order tracking system: CDUniverse provides Order
Status that informs consumers of the current status of
the albums they have bought.
In conclusion, the result of a close examination
of CDUniverse is illustrated with the following Figure. In Figure 3, T.F. indicates Transaction Flow and
Description summarizes the functions that CDUniverse provides or the actions that consumers take in
the business value chain. In addition, N/A indicates
Not Available.
CDUniverse Provides:
 (A1→A2→, …, →A20→A21) – By observing
Figure 4, the study finds that CDUniverse tries to
provide consumers with the most appropriate
products through A1 to A20, but it still cannot
satisfy all of its consumers in A15, A16, and A17.
Therefore, CDUniverse consumers have to select
their methods of payment and shipping in A18
and the result of their selections is never recorded.
Specifically, whenever regular consumers want to
pay a bill, they have to reselect their methods of
payment and shipping.
 (F1→A1), (F2→A2), (F3→A3), (F7→A7),
(F11→A11), (F15→A15), (F19→A19) – Newsletters (the advertising mechanism) inform consumers of the latest albums. By doing so,
CDUniverse can not only arouse consumers’ interests but also increase its sales. The multiple
sensor mechanism records the information about
consumers which is sufficient for the mechanism
to make more accurate inferences. The result that
the inferring mechanism infers enables consumers
to know better about the albums they desire. In
order to accelerate online transactions, the evaluating systems provide different approaches for
consumers to experience music online and to
learn the reviews from different points of views.
Furthermore, the order tracking system informs
consumers of the current status of the albums they
have bought to increase their satisfaction and their
willingness to repurchase. Undoubtedly, the information systems are the competitive advantages
of CDUniverse.
 (E2→A2),
(E3→A3→A4),
(E7→A7→A8),
(E11→A11→A12), (E19→A19→A20) –Two
experiments are conducted to respectively ex-
amine the shopping environment and the response
time in each of the information systems of CDUniverse. According to Kirk and Miller (1968), if
the response time is longer than 10 seconds delays,
users will turn to perform other tasks while waiting for the results from the computer. In the first
experiment, the result of the 100 randomly selected information systems is that all the response
time of them is shorter than 10 seconds. In the
second experiment, within 5 minutes, the study
randomly chooses the information systems to test
if their response time is shorter than 10 seconds.
Fifty percent of the response time is shorter than
10 seconds and fifty percent is longer than 10
seconds. Obviously, all of the tests receive response from the randomly selected information
systems. In this case, CDUniverse is a highly
available system and a highly reliable system.
Figure 3: Result of Examination After the examination, BMRS for CDUniverse is then conducted in the
Figure 4
193
Journal of Information Technology and Applications
Vol. 2, No. 4, pp. 189-195, 2008
5.
Conclusions
The study selects one successful online music
website to examine the feasibility of the proposed
model. Therefore, through the examination and
analysis of CDUniverse, the study finds that the
BMRS can be used to develop a recommender system.
According to the result of the analysis, these key
success factors include: (a) A recommender system
should develop the interaction that is built on a
learning relationship and is aimed at concluding a
transaction. The studied case, CDUniverse, does develop the interaction. (b) A recommender system
should provide a personalized environment. But,
CDUniverse does not follow this criterion. It is suggested that CDUniverse should provide a personalized environment based on consumer past buying
history and the inferring mechanism for methods of
payment and shipping to accelerate online transactions and to increase consumer satisfaction. (c) A
recommender system should be equipped with the
information systems to help support the learning relationship. CDUniverse is equipped with several information systems such as the advertising mechanism,
the multiple sensor mechanism, the inferring mechanism, the search engine, the evaluating systems, and
the order tracking system. (d) A recommender system
should provide a stable shopping environment and its
response time should be acceptable, such as shorter
than 10 seconds. CDUniverse does provide a stable
shopping environment and respond to its consumers
as soon as possible.
References
[1]
[2]
Figure 4: Result of CDUniverse Analysis
[3]
CDUniverse Does Not Provide:
 (A4→B4), (A8→B8), (A12→B12), (A16→B16),
(A20→B20), (A21→B21) – CDUniverse does
not provide each individual consumer with a customized environment based on his preference.
CDUniverse always tries to build a new relationship with its consumers, potential consumers and
regular consumers alike. Those consumers are
treated in the same way. Besides, CDUniverse
does apply consumers’ e-mail addresses and their
billing and shipping information in its strategy,
but it does not make good use of their past buying
history and their personal preferences.
[4]
[5]
[6]
194
Ansari, A., Essegaier, S., & Kohli,
R.,2000, ―Internet Recommendation Systems,‖
Journal of Marketing Research, 37 (3),
363-375.
Burke, R.,2002, ―Hybrid Recommender Systems: Survey and Experiments,‖ User Modeling and User-adapted Interaction, 12(4),
331-370.
Carbonell, L. Learning Theory. Retrieve December21,2004,from:
http://www.my-ecoach.com/idtimeline/learning
theory.html
Chen, P. –L, 2002, ―Planning and Design of
Database for e-Clerks - An Implementation on
Online Bookstores,‖ Master Thesis of Institute
of Information Management in National Sun
Yat-sen University. Taiwan: Kaohsiung.
Engel, J. F., Blackwell, R. D., & Kollat, D. T.,
1986, Consumer Behavior. (5th ed.). New York:
Dryden Press.
Gupta, A., Su, B., & Walter, Z., 2004, ―An Empirical Study of Consumer Switching from Traditional to Electronic Channels: A Pur-
Journal of Information Technology and Applications
Vol. 2, No. 4, pp. 189-195, 2008
chase-Decision Process Perspective,‖ International Journal of Electronic Commerce, 8,
131-161.
[7] Herlocker, J. L., Konstan, J. A., & Riedl,
J.,2000, ―Explaining collaborative filtering
recommendations,‖ Proceeding on the ACM
2000 Conference on Computer Supported Cooperative Work (pp. 241-250). Philadelphia:
PA.
[8] Kirk, J., & Miller, M. L. , 1986, ―Reliability
and validity in qualitative research,‖ Beverly
Hills: Sage Publications.
[9] Lee, W. –P., Liu, C. –H., & Lu, C. –C. ,2002,
„Intelligent agent-based systems for personalized recommendations in Internet commerce,‖
Expert Systems with Applications, 22,
pp.275-284.
[10] Mahmood, M. A., Bagchi, K., & Ford, T. C. ,
2004,
―On-line
Shopping
Behavior:
Cross-Country Empirical Research,‖ International Journal of Electronic Commerce, 9(1),
9-30.
[11] Pine II, B. J., Peppers, D., & Rogers, M., 1995,
―Do you Want to Keep Your Customers Forever?,‖ Harvard Business Review, Mar-Apr.
103-114.
[12] Popescul, A., Ungar, Lyle H., Pennock, David
M., & Lawrence, S.,2001, ―Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data
Environments,‖ Proceedings of the Seventeenth
Conference on Uncertainty in Artificial Intelligence (UAI-2001) (pp. 437-444). San Francisco:
Morgan Kaufmann.
[13] Sarwar, B.M., Karypis, G., Konstan, J.A., &
Riedl, J. ,2000, „Application of dimensionality
reduction in recommender systems—a case
study,‖ ACM WebKDD 2000 Web Mining for
E-Commerce Workshop. Boston: MA.
[14] Schafer, J. B., Konstan, J., & Riedl, J. ,1999,
„Recommender Systems in E-commerce,‖
Proceedings of the ACM Conference on Electronic Commerce, pp. 158-166.
[15] Seetoo, Dah Hsian. ,2001, ―Strategic Management: A New Perspective for Analysis,‖ Taipei:
BestWise Co., Ltd.
[16] Weng, Sung-Shun., & Liu, Mei-Ju. ,2004,
„Feature-based
recommendations
for
one-to-one marketing,‖ Expert Systems with
Applications, 26, 493-508.
195