MODELING PREFERENCES ONLINE

Maria Cleci Martins

and Rosina Weber

College of Businees, Brazilian Lutheran University R. 15 de Novembro, 253 Novo Hamburgo, RS 93315 Brazil

College of Information Science & Technology, Drexel University 3141 Chestnut Street Philadelphia, PA 19104 USA

Keywords: E-Commerce, Discrete choices, Stated preferences, Personalization, Ontologies, User modeling

Abstract: The search for an online product that matches e-shoppers’ needs and preferences can be frustrating and

time-consuming. Browsing large lists arranged in tree-like structures demands focused attention from e-

shoppers. Keyword search often results in either too many useless items (low precision) or few or none

useful ones (low recall). This can cause potential buyers to seek another seller or choose to go in person to

a store. This paper introduces the S

POT (Stated Preference Ontology Targeted) methodology to model e-

shoppers’ decision-making processes and use them to refine a search and show products and services that

meet their preferences. SPOT combines probabilistic theory on discrete choices, the theory of stated

preferences, and knowledge modeling (i.e. ontologies). The probabilistic theory on discrete choices coupled

with e-shoppers’ stated preferences data allow us to unveil parameters e-shoppers would employ to reach a

decision of choice related to a given product or service. Those parameters are used to rebuild the decision

process and evaluate alternatives to select candidate products that are more likely to match e-shoppers’

choices. We use a synthetic example to demonstrate how our approach distinguishes from currently used

methods for e-commerce.

1 INTRODUCTION

The search for an online product that matches e-

shoppers’ needs and preferences can be frustrating

and time-consuming. Information about products

and suppliers is usually accessed from database

servers using either list browsing or keyword search.

However, the amount of information available in

those databases has substantially increased the

cognitive effort required for e-shoppers to make

their choices. Browsing large lists, arranged in tree-

like structures can be time consuming, while

keyword search often results in too many useless

items and too few actually useful (or none) being

returned. Thus, instead of facilitating the choice (and

the sale), the Internet makes the e-shopper’s choice

decision-making process more difficult. Such

difficulty is frustrating and is detrimental to online

sales.

Addressing customers’ needs is crucial for e-

commerce. E-commerce systems should be able to

facilitate the customers’ choice process by offering

alternatives that are more likely to satisfy their

preferences. This would generate less frustration and

potentially increase revenues, service level and

customer’s satisfaction.

Personalization is an approach that uses

characteristics of individual users to select

information to be searched and displayed to users

(Cotter & Smyth 2000). Recommender systems for

e-commerce (e.g., Ardissono & Godoy 2000;

Domingue et al. 2002, Burke 2000) address the

personalization issue by filtering the amount of non-

requested products to be showed to the e-shopper in

a given session. Recommender systems can be

collaborative, content-based, demographic, utility-

based, and knowledge-based (Burke 2002).

Recommender systems are useful when

customers do not know exactly what product or

service they need, or when the company wants to

introduce different products to the user. However,

when customers roughly know their needs and the

type of product or service to address those needs, the

problem is to find the best available online option

according to the user’s viewpoint. This problem is

typically addressed only by utility-based

recommender systems (Burke 2002). As a

comparison, in a physical store, the shopper would

be able to use other senses (e.g. vision, touch) to

recognize available products and compare them

255

Cleci Martins M. and Weber R. (2005).

MODELING PREFERENCES ONLINE.

In Proceedings of the First International Conference on Web Information Systems and Technologies, pages 255-262

DOI: 10.5220/0001230702550262

 SciTePress

before choosing one, or ask a sales person for

advice. On the Internet, however, they have to rely

on their decision-making skills and the available

information to choose the best option.

This paper proposes the use of the economic

theory on discrete choices (Ben-Akiva & Lerman

1985) to help e-shoppers find the best match for

their needs from what is available on the Internet. In

this sense, it can be categorized as an utility-based

recommender. Our approach is to elicit from e-

shoppers how they make choices, build a model of

their choice behaviour, and use it to refine the

search and show products and services that meet

their preferences.

Discrete choice modeling has been largely used

in the transportation field to forecast travel demand

from disaggregate data on individual choices (Ben-

Akiva & Lerman 1985; Fowkes & Shinghal 2002).

For example, it is used to forecast demand by

finding the likelihood that a travel mode is chosen

given certain characteristics such as travel time,

comfort, and headway. The rationale for using

discrete choice modeling is that it is a mature

methodology to uncover users’ decision-making

processes without asking them directly.

The mathematical model − Logit is very robust

and it is likely that the user’s decision-making model

found is the best possible (Ben-Akiva & Lerman

1985). Alternative methods (e.g. non-linear) are

computationally more complex, more demanding to

the user, and their result has been shown to be only

marginally better (De Carvalho, M. 1998).

Section 2 reviews methods used in this work:

discrete choice modeling, stated preference,

ontologies, and personalization. The S

POT

methodology is described in Section 3, followed by

a demonstration that uses statistics-based simulation

in Section 4; Section 5 is a discussion and Section 6

concludes.

2 METHODS

2.1 Discrete Choice Theory

The term choice refers to the cognitive process of a

consumer who, after evaluating the alternatives in a

choice set, decides to select one of them (Louviere

1988). Discrete choice modeling is a well-known

and mature methodology (Ben-Akiva & Lerman

1985) to investigate that process. The main feature

of discrete choice data is that the observed response

(i.e. the dependent variable) is discrete: the method

only determines whether or not customers choose

one alternative option.

Discrete choice models can use compensatory or

non-compensatory rules. Compensatory models

allow offsetting changes in one or more attributes to

compensate for a change in a particular attribute

(implying simultaneous consideration of all

attributes). For instance, a roomier seat can

compensate for a higher price in air travel. By

contrast, non-compensatory models do not permit

trade-offs between attributes; comparisons are made

on a sequential consideration of each attribute. The

last decision is often based on a compensatory

model to compare final options (if more than one).

This is the decision-making situation faced by e-

shoppers on the Internet.

Discrete choice modeling is based on the

economic utility theory for compensatory models

with the following four assumptions about the

consumer behaviour.

1. Products or services can be represented in vectors

of feature-value pairs (attributes), e.g. cost, brand.

2. Customers are optimizers and they compare

options based on the value of their attributes.

3. Customers make trade-offs between attributes of a

product/service to reach their decision, e.g. in

transport service, less comfort can be accepted if the

fare is reduced.

4. Customers are maximizers and they always

choose the best perceived option within a knowledge

domain.

2.2 Stated Preference

Stated Preference (SP) is a technique used to collect

data on individual’s discrete choices (Pearmain &

Kroes 1990). It can be understood as a simulation

game where individuals are asked to state their

preferences for a set of possible options (i.e. choice

set). A choice set is composed of at least two

alternatives e.g. a trip can be characterized by the

attributes cost and in-vehicle travel time. A choice

set would consider the transportation modes car and

train, each mode being represented by its respective

cost and travel time. The number of choice sets is

developed according to the number and levels of

attributes to be considered.

The design of a SP collection must consider

trade-offs between attributes of the product or

service. Respondents should be given choice sets

with possible options, but it is not necessary to know

exactly which options are available and the exact

values of the attributes; the attribute values should

be as close to reality as possible. An Internet

collection can be designed at runtime (e.g. using the

support of a knowledge base).

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A desired property of a SP collection is

orthogonality (zero correlation between attribute

values and alternatives), so that separate effects on

choices can be estimated, as well as possible

interaction effects of their combinations. For the

sake of demonstration (Section 4) we are employing

a full factorial design that guarantees orthogonality

(Kocur et al. 1982). On a real situation, fractional

designs have to be employed to reduce the

respondents’ fatigue. Factorial design provides a

way to investigate the interaction effects between

attributes, such as price and travel time. To measure

all interaction effects one should use a full factorial

design, which is a problem that grows exponentially.

Fractional factorial designs are employed to reduce

dimension and the number of alternatives users have

to analyse. In that case, some minor interaction

effects are ignored in the experiment.

In our proposed approach, customers’ stated

preference data is used to calibrate a Logit model

(Ben-Akiva & Lerman 1985) that will unveil the

parameters (weights) that the e-shopper would use to

evaluate and choose one online option.

2.3 Logit Modeling

Logit modeling assumes that options are represented

by a function (U

) composed of unobserved variables

(β

), which are somehow associated with

characteristics (X

) of the product (i) and a random

term (ε)(See Equation 1). The function U might be

continuous or not, depending on the type of the

attributes. If price is continuous and colour is

discontinuous, then a function with both these

variables would necessarily be discontinuous at

some point. The values of the coefficients are found

from data containing trade-offs between attributes

that are therefore incorporated into the modeling.

),(

εβ

ijji

XfU =

Equation (1)

The coefficients (β) on the observed characteristics

) in the utility function (U

) are estimated with an

optimization procedure such as Newton-Raphson

(Ben-Akiva & Lerman 1985). The exponential

behaviour (e) is employed to explain predicted

probabilities (P

) of a particular response (“buy” or

“not buy”) regarding an alternative “i” (See

Equation 2) belonging to the choice set with “J”

options. Thus, the likelihood that an alternative is

chosen is expressed as a function of its attributes and

the other options available in the choice set.

∑

Equation (2)

As maximizers, individuals place their preferences

in the alternative they recognize as having the

highest utility value (U

> U

). The analyst uses the

modeling approach to be able to find the likely

coefficients underneath the decision that has

determined the choice. Considering that some of the

variables influencing the choice might not have been

accounted for, a random term is added to the model.

In case of Logit, Luce (1959) has shown that the

random term is independent and identically

distributed according to the Weibull distributions.

This means that alternatives are uncorrelated and

also independent. We will use this characteristic of

the model as the base to create synthetic data and

demonstrate our approach (Section 4).

2.4 Web Personalization

Web personalization is concerned with schemes that

select the type and quantity of content to be shown

to the e-user based on individual profiles.

Personalization applications for e-commerce usually

show products and services the e-shopper did not

ask for, hoping that some of them will catch his or

her attention.

Content-based filtering makes recommendations

based on comparisons between resources and the

user’s profile. Results retrieved are based on their

similarity to what the e-shopper has previously

shown interest. Collaborative filtering selects

products or services that are recommended or used

by the e-shoppers’ peers by identifying groups of

Figure 1

The SPOT methodology

Knowledge

Base

Web Client

Mathematical Module

user

Web

Resources

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257

U(x

)

User’s options for

keyword input

Utility curve (search space)

Figure 2: Pictorial representation of utility function

(U(x))

users with similar characteristics and interests

(Cotter & Smyth 2000).

The approach in this paper can be considered

both utility and knowledge-based. Utility-based

because it models utilities of an option; knowledge-

based because it proposes the use of ontologies for

representing knowledge related to online shopping.

Ontologies are knowledge models that retain

conceptualizations that are explicit, consensual, and

conceptual (Gruber 1993). A

LICE (Domingue et al.

2002) is an example of an ontology-based

recommender system.

3 SPOT METHODOLOGY

The Stated Preference Ontology Targeted (SPOT)

methodology (Figure 1) for web personalization uses

the implicit user’s decision function to find the

product or service with the highest likelihood of

being considered by the e-shopper in a given e-

session. While keyword search methods use words

to find related information, S

POT uses the

individual’s decision function (i.e. utility) to search

the web space and find appropriate offers. Figure 2

is a pictorial representation of the search space, i.e.

data points and extrapolation points. One can

understand those points as choice possibilities or

products. The approach suggested in this paper

builds a user profile based on the individual’s utility

curve, instead of those based on isolated points

whose matching product options might not exist.

The core of the methodology is stated preference:

the technique employed to collect individual data on

discrete choices (i.e. how individuals make

decisions). Once enough data is collected, the model

is calibrated using Logit modeling. The results are

coefficients relating product attribute values and

their importance to the users. Those coefficients are

then used to rebuild the utility function for each

alternative of product available online. Those with

the highest likelihood value should be shown to the

user. The two main modules in S

POT are the

knowledge base and the mathematical module. The

knowledge base retains ontologies, (e.g. products,

customers’ profiles, communities); the mathematical

module manipulates algorithms for modeling the

discrete choice data, and for analysis of the results.

3.1 Knowledge Base

We are assuming that within the semantic web,

products and services will be described using

product ontologies. Standards for defining and

classifying goods have already been developed, such

as ISO 10303 (step) and can be used as the basis of

products ontologies. Such ontologies will contain

links to web pages of those companies providing the

service, and to product attributes that customers

might consider important (and therefore use in their

decision-making).

The ontology-based recommender system A

LICE

(Domingue et al. 2002) includes ontologies for

customer, products, typical shopping tasks, and the

external context. Ontologies are populated as they

are linked to the company’s databases. Two

important ontologies in A

LICE are Customers and

Products. Customers ontology defines slots about

customers (their typology, how they use the product,

which attributes are important, etc.). The Product

ontology contains information about the product,

such as type and attributes.

As product ontologies grow, so does the need for

more sophisticated methods to select products to

offer to users. The S

POT methodology can be

implemented on top of e-commerce systems such as

LICE (ibid) to address the selection problem. E-

shoppers’ preferences and information about their

decision-making processes would be part of the

customer’s ontology.

Another characteristic of incorporating

knowledge bases to e-commerce systems is the

potential to systematically discover knowledge from

collected data. Kozinets (1999) suggests the

identification of true communities of consumption

by clustering information on individuals’ profiles i.e.

gathered in their buying decision-making processes.

4 SYNTHETIC STUDY

We demonstrate our approach using a theoretical

example of an online search situation where we

compare keyword search with S

POT.

4.1 Methodology

A factorial design was used to gather choice answers

for a simulated customer. The full factorial design

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guarantees that calibration results are significant.

Alternative options were built with the purpose of

showing how the proposed methodology compares

with the traditional database search. The data

contains choice sets with three alternatives each,

which are evaluated in three attributes (Table 1:

Attributes 1, 2, and 3). For instance, a transport

option could be characterized by its cost, travel time,

and headway. High and low (Table 1) indicate

extreme ranges for the options.

Table 1 Attribute levels for the 512 choice scenarios

Alternative 1 Alternative 2 Alternative 3

low high low high low high

Attribute

30 80 40 100 50 120

Attribute

20 45 15 30 10 40

Attribute

30 15 60 20 70 30

The synthetic data is created based on a full factorial

design so that our simulated customer made 512

hypothetical discrete choices. Of course, in a real

situation there are other methods (Fowkes &

Shinghal 2002) that can be employed to reduce this

number to an acceptable value and still show good

calibration performance.

The simulation approach is based on the fact that

we know the deterministic part of the utility function

used by decision makers in a choice situation. The

random term is the unknown part of the utility but

we know its distribution mean and standard

deviation. The total utility for each alternative is

found by adding the random term (Weibull

distributed) to the deterministic utility component

(see Equation 1). A linear function that adds the

option attribute values by its respective weight is

employed to find the deterministic component. The

probabilistic part is simulated using the method of

the inverse function. Thus pseudo-random numbers

are created according to the inverse of the Weibull

probability distribution (Equation 4) and used as the

behaviour of our simulated individual regarding his

choices. Following is a brief explanation of the

Weibull probability distribution as the base to create

data that follows Logit assumptions.

4.1.1 Weibull Probability Distribution

The random part of the utility refers to unknown

variables influencing the choice process, from the

analyst point of view. For instance, taste variation.

Logit modeling is based on the assumption that such

random term is Weibull (or Extreme Value)

distributed (Luce 1959) as in Equation 3. Therefore,

knowing the inverse of the cumulative Weibull

distribution function (Equation 4), it is possible to

recreate a SP experiment synthetically. This

procedure allows one to compare methodologies on

the bases of what the answers would be.

e()

()

−

−−

Equation(3)

Assuming η=0, the inverse of that function

(Equation 4) results in a random number (ε) which is

Weibull distributed. This number would account for

the uncertainties in the modeling a process analysts

do not know (though known by the decision-maker).

−−ln( ln( ))

, =

Equation(4)

Where u = a uniformly random number; σ =

standard deviation; η = location parameter.

Table 2: Calibration results for SP discrete choice data

Likelihood -176.7801

Rho-Squared 0.6857

N. iterations 7

Coefficients

Attribute 1 -0.03697 (-9.2)

Attribute 2 -0.03057 (-12)

Attribute 3 -0.03032 (-5.5)

The synthetic data (composed of 512 choice

scenarios and the choice) is then used to calibrate a

Logit model that reveals the weights the customer

used to make the choice. The performance of the

calibration is investigated using a well-known

econometric test, Rho squared. Results from

calibrating our synthetic data are shown in Table 2.

Rho squared is quite high and coefficients are

significant, as expected (since we are using data that

follows Logit modeling).

Table 3: Choice options and respective utility values

Option

Attr. 1 Attr. 2 Attr. 3 Utility*

30 20 15 -7.6779

30 45 30 -15.7752

100 30 20 -13.4744

40 15 60 -7.8835

120 40 30 -17.574

50 10 70 -7.0279

50 10 30 -5.8151

50 40 70 -16.1989

50 40 30 -14.9861

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Additionally, coefficients used to create the data

could be roughly recovered. Therefore, we are using

these coefficients to evaluate the alternative options

in Table 3. Observe that in our special case we know

the true coefficients employed to create the data. In

real situations those coefficients are only known

employing a mathematical model. A major

advantage of using synthetic data is that we know

beforehand the deterministic part of the utility

function and the parameters used to create the

random part. Then, we can evaluate the results

comparing them with the known function used to

create the data. The following tables show the results

from calibrating the SP synthetic dataset.

4.2 Results

Table 3 shows for each option (1 to 12) their

attribute values and their respective utilities. For

instance, the values of Attributes 1, 2 and 3 for

Option 1, are 30, 20 and 15, respectively. In case of

a transport option it could be 30 minutes travel time,

20 minutes waiting time, and price of 15 USD, or

nominal values. Table 3 is the database of available

options for a searching system.

Given the database of 12 possible choices shown

in Table 3, we examine Situation 1 and Situation 2,

where we employ respectively keyword search and

POT. Results are shown in Tables 4 and 5.

Situation 1

: The user inputs a keyword that matches

at least one of the available options. For instance,

value of 40 to Attribute 1.

Table 4: Results for Attribute 1 = 40

Method Result shown to the user

Keyword search Alternative 7 (40, 15, 60)

SPOT Alternatives 9, 10 and 1

Keyword search shows Alternative 7 as its own

possible match. S

POT methodology using the value

of the utility of all alternatives, would show three

results corresponding respectively to 1

, 2

, and 3

places.

Situation 2

: The same user inputs a keyword that

does not match any of the available options (quite

common on the Internet for travel services like car

rental). For instance, value of 60 to Attribute 1.

In Situation 2, the keyword search method does

not return any possibility. On the other hand, S

POT

methodology returns 3 possible alternatives. In this

case, we are employing a compensatory model and

the three attributes are evaluated at once. However, a

non-compensatory model can also be employed to

perform a pre-selection of maximum or minimum

attribute values. As an example, the user would not

accept to pay more than US$ 50 for the trip.

Table 5: Results for Attribute 1 = 60

Method Result shown to the user

Keyword search None

SPOT Alternatives 9, 10 and 1

Given results shown on Table 3, the best choice

from the customer decision-process viewpoint would

be Option 10 (the highest utility), which is

highlighted.

In this theoretical example, we illustrated how

using knowledge about the user’s decision-making

process can improve the quality of the online search

results. For instance, in case of Situation 1, only one

alternative would be shown to the user (Option 7 in

Table 3). This alternative would not even be

considered by the user as there are others with

higher utility value (Table 3). On the other hand,

Situation 2 would show no results to the user; as the

criteria do not match any of the alternatives in the

database (Table 5). This is quite a common situation

in e-commerce sites.

5 DISCUSSION

A recommender system is one that, based on certain

criteria, recommends products or services. Current

personalization schemes are mainly focused on

delivering contents that are either similar to users’

profiles (i.e. content-based) or are recommended by

their peers (i.e. collaborative). Information on e-

shoppers (e.g., history, profile, preferences) is used

to feed the personalization scheme. A

comprehensive review of recommender systems is

given by Burke (2002). Being utility-based, this

paper addresses a slightly different problem: how to

help the e-shopper decide between the choices

available on the Internet.

Usually, the information gathered over the

Internet from recommender systems is not used for

other purposes than to feed the personalization

scheme. These schemes do not address ways to

improve the company’s decision strategies (such as

product design), or how it could help the e-shopper’s

choice decision-making process. Helping the e-

shopper in this decisive moment has the potential

not only to increase the company’s sales but also to

improve the knowledge about their customers’

values. That is often a strategy used in physical

stores where the sales person often has a decisive

impact on the choice.

Figure 3 illustrates a real situation of online car

rental pictured on a shopbot web page. Shopbot is an

e-commerce portal where users have access to

different web service providers and can compare

their offers as well as buy them. In this web site, the

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Figure 3: Online car rental shopbots

e-shopper begins the search process filling out a

form with some parameters (e.g., car size, pick-up

day, pick-up location). Those parameters are used to

search the server database for available options.

Quite often the search is unsuccessful at the first

time. There are different reasons for that. For

example, the specific supplier may not have

branches on the pick-up location, or the requested

car size is not available. Eventually, the user has to

change the search parameters a couple of times in

order to find one offer. When the user finally

manages to find some offers, she or he has to reason

and decide for one of them or none. It might be the

case that by evaluating the choices available, the

user considers that all offers are overpriced

compared to the prices of the cars and decides not to

buy the service. Therefore, instead of hiring a car,

the individual might decide to use local public

transport, or a taxi service. Note that decision

processes vary according to the individual and the

situation. Whether the individual is shopping for

himself or for a company may change the decision

model.

From the perspective of the car rental company, it

could be a lost sale. If only the car-rental company

new how individuals evaluate the different attributes

of the service, they would try to show alternative

options from the customer perspective. Maybe

showing an offer with a better car would give the

correct balance between the price of the car and the

rental value.

In the example above, we are assuming that the

user evaluates the car rental options considering the

price of the vehicle being hired and alternative ways

of transport (such as public transport and taxi). Other

decision models for this service would consider car-

size within an acceptable price-range. In the car

rental business, companies are often bounded to

specific carmakers. Moreover, they have prices tied

to combination offers that force the consumer to

purchase at least two services. A web portal offering

such car rental services would benefit from the S

POT

approach, as it would always show options regarded

by the user as relevant. With currently used

methods, the search usually has to be repeated a

couple of times with different keywords before a

reasonable option comes up as a result.

Furthermore, the S

POT methodology is based on

knowledge about how customers evaluate product

characteristics, e.g. what sort of decision process

they perform, which attributes and variables they

consider. This is an application with potential to take

full advantage of the semantic web infrastructure. It

can search semantic information on products (i.e.

from products ontology) and service information,

and populate ontologies on customers’ profile.

Although ontologies are seen as the core of the

semantic web, actual applications are still in their

infancy. An initiative for transforming knowledge

about products and services into a world common

ontology is ISO 10303, an International Standard for

product data representation and exchange. However,

there is still need for technologies that enable

application systems to exchange and share data

about technical products. Their product

classification cannot be used as a complete ontology,

as the definitions tend to be semantically weak.

6 CONCLUSIONS

The SPOT methodology discussed in this paper uses

the evaluation of the online alternatives based on the

e-shopper decision process. This personalization

scheme will prompt advantageous options that the e-

shopper would not find otherwise. The main purpose

of S

POT is not to make recommendations on

products that users may or may not be interested in.

POT’s main contribution is to help the user with the

decision-making on products he needs but have

difficulties choosing between the large amounts

available on the Internet. This approach has the

potential to substantially improve the relevance of

the results shown to the e-shopper in an e-commerce

session and therefore increase the likelihood of a

sale. Even though the user would be anonymous

during the session, results from the system allow the

company to know the trade-offs individuals make

between the characteristics of a product or service

and use them to forecast online demand, improve

products, etc.

This paper discusses a methodology that uses

economic theory on discrete choices to link e-

shoppers’ decision-making process to available

online options. The approach suggested in this paper

builds a user profile based on the individual’s utility

curve, instead of isolated points (the user’s criteria)

whose options might not exist.

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The main input to the proposed methodology is the

discrete choice data, which is collected from

interactive Stated Preference “games” that the e-

shopper agrees to participate. The data is then used

to calibrate a Logit model that will reveal the trade-

offs the e-shopper employed in his or her choice

decision-making process. Afterwards, these results

are employed to search for the available options and

calculate their values as the customer himself would.

Options with high utility value are then shown to the

e-shopper.

The benefits of using the methodology are

twofold. First, it has the potential to increase

customer satisfaction and therefore the likelihood of

sales and revisits. Second, the information on

customer’s choice decision-making process gives the

company insights on how to improve the business

(such as product design and sales). The

implementation of this methodology requires

investigation of the user’s decision-making process

for each product and the development of friendly

interfaces to reduce the time to collect stated

preference data online.

The major challenge of implementing S

POT is the

data gathering. The approach’s input is data from an

interactive SP game that demands customers’ time.

Customers have to be convinced that providing

answers to the interactive game will give them a

better service. A friendly interface can help

overcome this problem by reducing the cognitive

effort needed for the task.

Another alternative would be to insert an

additional reasoning step and try to match a current

e-shopper with a previously recorded decision-

making model. This match could be based on

similarity (i.e. using case-based reasoning) and

would reduce the number of required questions to

elicit the e-shopper’s preferences.

We should also consider that customers may not

be interested in wasting time to take part in a SP

game that evaluates low priced products or services.

This requires an analysis of the customer’s value of

time to discover the threshold from which they

would be willing to compare options further. As a

guideline, the company could employ this

methodology only to the most profitable or high

priced 20% products, which often represents

approximately 80% of the company’s profit.

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