ENHANCING RECOMMENDER SYSTEMS DEVELOPMENT

WITH HUMAN, SOCIAL, CULTURAL AND ORGANIZATIONAL

FACTORS

Andreas S. Andreou

Department of Electrical Engineering and Information Technology, Cyprus University of Technology

P.O.Box 50329, 3603 Limassol, Cyprus

Stephanos Mavromoustakos

Department of Computer Science and Engineering, European University Cyprus, P.O.Box 22006, 1516 Nicosia, Cyprus

Keywords: Web Recommender Systems, Human, Cultural, Social and organizational factors.

Abstract: Recommender Systems (RS) aim at suggesting filtered Web information adapted to the needs or interests of

users by predicting their access behavior using a certain strategy or algorithm. The creation of RS is usually

approached focusing mostly on user behavior modeling, while the recommendation engine often neglects

critical, non-technical aspects of software systems development. Conceiving a RS primarily as a self-

contained or part of a Web-application, the present paper utilizes the SpiderWeb methodology and takes

into account important requirements that result from human, cultural, social and organizational factors

(HSCO) so as to drive the RS development activities.

1 INTRODUCTION

Recommender Systems (RS) are a special class of

software applications usually running on the Web to

support users in making decisions while interacting

with large spaces of information (Chen, Shtykh and

Jin, 2009). RS recommend subjects or items of

interest to users based on information that may be

categorized in two main classes, the first being

gathered from the users explicitly through

interactive collection means (e.g. dialoguing or

conversational modules) and the second being

implied using various modeling and prediction

techniques of user behaviour (Baraglia and Silvestri,

2007).

While there is a plethora of research works

dealing with RS development the majority focuses

on ways to implement the recommendation engine

based purely on technical development factors.

Nevertheless, a RS may be greatly enhanced if

certain factors falling into a non-technical sphere are

taken into account prior and during its creation.

These factors mostly describe human, social,

cultural and organizational (HSCO) aspects which

may complete the picture for understanding the

behavior, as well as the expectations of users

working on the Web, thus contributing to better

modeling certain behavioral characteristics and

ultimately leading to finer information

recommendations. In this context we will view RS

as a specific-purpose (or part of a) Web application

and we will provide a methodology for identifying

and analyzing certain HSCO factors which will be

incorporated in the RS thus enhancing its efficacy.

The methodology utilizes the SpiderWeb model

proposed in (Andreou, Mavromoustakos and

Schizas, 2002), which records critical factors that

must be incorporated as functional or non-functional

features in the RS under development.

2 THE SPIDERWEB MODEL AND

THE RS INFORMATION

GATHERING METHODOLOGY

The SpiderWeb model aims at visualizing and

classifying valuable requirement components for the

211

S. Andreou A. and Mavromoustakos S..

ENHANCING RECOMMENDER SYSTEMS DEVELOPMENT WITH HUMAN, SOCIAL, CULTURAL AND ORGANIZATIONAL FACTORS.

DOI: 10.5220/0003553202110214

In Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS-2011), pages 211-214

ISBN: 978-989-8425-56-0

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

better identification of critical factors that will lead

to the development of successful software systems

running on the Web. In our case, success is assumed

when the RS is able to provide information

suggestions that are very close to the user interests,

characteristics and expectations at the time of

accessing particular portions of Web information.

The model categorizes system requirements in three

main axons: The Country Characteristics, the User

Requirements, and the Application Domain axon

(fig. 1).

Figure 1: The SpiderWeb Model.

Each axon includes certain components, which are

directly connected and interrelated. The SpiderWeb

axons are also interdependent, allowing the sharing

of same, similar, or different characteristics among

each other.

The analysis of the axon components of the

SpiderWeb model presented in the previous part

aimed primarily at providing the basic key concepts

for collecting proper system requirements. These

concepts are to be used as guidelines for gathering

critical information that may affect the functional

and non-functional behavior of the RS under

development. A form of small-scale ethnography

analysis is conducted for collecting and analyzing

information for the three axons described before.

Our method includes focus questions produced

in the form of questionnaires. These questions are

distributed among the targeted group or are used as

part of the interviewing process, and the answers are

recorded, analyzed and evaluated.

The SpiderWeb methodology is integrated with

the WebE process (Pressman, 2000), the latter being

used for the development of Web applications.

As previously mentioned, the SpiderWeb model

is utilized to guide the creation of the

recommendation engine so as to provide the right

recommendations. The engine employs both offline

and online data gathering and processing procedures

as follows:

Offline Operation Procedures

During the Analysis Phase the following offline

operation procedures are used:

Step 1 – Administer focus questions to groups of

users and collect HSCO information on country

characteristics, user requirements and the RS

itself.

Step 2 – Estimate the preferences’ priorities (e.g.

purchasing decisions or searching patterns)

according to the user groups and the type of the

application

Step 3 – Compute similarity measures between

user groups

Step 4 – Derive the possibility measures

Online Operation Procedures

Step 1 – Set up basic parameters by establishing

an initial dialogue with users to collect HSCO

information (e.g. age, gender)

Step 2 – Record on-click and identify users’

interests and preferences patterns

Step 3 – Analyze search and browse patterns,

build categories

Step 4 – Match related content with categories

Step 5 – Provide recommendations

First, historical data are selected and added into

datasets. For every search, if frequently occurring

patterns (classifiers) are found then those of good

quality are used for recommendations. When new

information is requested, the system identifies the

corresponding class labels using multiple classifiers.

Finally, the performance of the RS is evaluated by

investigating the accuracy of the recommendations

offered.

Typically, a client is accessing the RS through

her/his Web browser where she/he can search and

retrieve information (fig. 2). The Web server will

receive her/his request and information and then

process the data. The requirements pre-processing

subsystem will receive the request and search the

semantic rule database and if there is a relationship

then there is a match to the original requirements.

Offline classifiers are built and stored in the

classifier rule database for new classifications. For

every new customer requirements, the server will

find all the classifiers that meet the conditions of

these requirements by searching the classifier rule

database and the new recommendations are

provided.

Country

Characteristics

User

uirements

Application

Domain

1 2 . . .

…

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Figure 2: The 3-Tier Architecture of our RS.

3 RS CASE STUDY

We have developed an application to provide

information on treatments and health care providers

around the world based on patients’ requests for

medical tourism. Finally, we enhanced the system by

adding a RS to provide more options to patients on

health care providers as well as treatments.

RS are typically either homogeneous (i.e.,

content-based filtering) or heterogeneous (i.e.,

collaborative filtering) for product recommendations

(Yuan and Cheng, 2004; Schafer, Konstan and

Riedl, 1999). The Content-Based Filtering (CBF)

approach recommends products to target customers

according to the preferences of their neighbors while

the Collaborative Filtering recommends products to

target customers based on their past preferences.

We have developed a hybrid RS based on CBF and

CF approaches. Therefore, we combined both

collaborative and content association rules to form

hybrid association rules.

Focusing on the HSCO factors we have isolated

three such factors for demonstration purposes,

namely Gender, Age and Country. A user is

searching the site on the treatment and destination

he/she is interested e.g. Cosmetic Surgery in Cyprus

or browsing the site for the treatment he/she is

interested (fig. 3). The user may select one of the

available providers to check his profile information

such as, description, facilities, medical team, etc.

The RS system provides recommendations for more

available related treatments (on cosmetic surgery)

offered by the provider. In addition, he/she will

receive recommendations on more available

providers that offer the treatment requested, in this

case cheek implants.

If we now take into account the aforementioned

three HSCO factors then recommendations become

more accurate and closer to the user (hidden)

preferences. According to user’s profile the system

provides some recommendations that are directed

towards that specific gender like Masks, Lipsticks,

Makeup etc (fig. 4a). According to Age, a simple

rule inserted into the system as a result of the pre-

processing stage the system suggests certain types of

cosmetic surgery or/and treatments that are highly

popular among a certain range of age, like face

lifting, botox, breast augmentation or lift, etc. (fig.

4b).

Figure 3: Browsing Cosmetic Surgery Treatments.

Country characteristics may also lead to

suggesting other sources of information, like site

seeing in the specific country, monuments etc (fig.

4c). The recommendations are also prioritized based

on the city of his/her choice and neighboring cities

within the country of origin based on distance

proximity.

The system was validated using a population of

75 people both male and female persons, with

average age of 43 years old.

A questionnaire was distributed to the population

to evaluate the effectiveness and efficiency of the

RS. The responses of the users revealed the correct

nature of the recommendations which were made

based on the HSCO factors gathered via the

SpiderWeb. Female subjects of our population were

Client

Web browser

Pre-processing

Classification

Tier 1 Tier 2 Tier 3

DB Rules

Web server

ENHANCING RECOMMENDER SYSTEMS DEVELOPMENT WITH HUMAN, SOCIAL, CULTURAL AND

ORGANIZATIONAL FACTORS

213

Figure 4: Recommendations of information based on (a) Gender, (b) Age, (c) Country.

excited to read the recommendations on cosmetics,

fashion and the alternative treatments (surgical)

suggested by the system. The male subjects did not

receive any such recommendations and were

satisfied they did not have to go through

information of no practical benefit to them. In

addition, ratings of the recommendations ranged

between good and excellent, with people finding

very helpful the suggestions of the system,

understandable and clear. It is worth noting that 5

out of our 35 female persons were reluctant to give

their age (and did not actually provide this data till

the end of the session) as they felt it was a

sensitive personal information. Fortunately, 4 of

our remaining female persons that actually inserted

their age to the system were over 40 and thus they

received the information regarding additional

treatments based on their age range, something

which was found at first amusing but also quite

interesting as these specific persons had limited

knowledge on the suggested therapies. Overall, 72

people replied that they prefer to use a Web

application with recommendations.

4 CONCLUSIONS

In this paper we have proposed a method based on

the SpiderWeb model for identifying significant

human, social, cultural, and organizational

(HSCO) requirements for enhancing the

development of RS.

We have developed a small pilot application

with which the efficacy and efficiency of a RS that

takes into consideration HSCO factors was

demonstrated. A short scale user validation

suggested that indeed when taking such factors

into consideration for producing recommendations

satisfaction between users is greatly enhanced as

the time to search for and locate useful information

is minimized and often the suggested information

broadens the views of users on certain subjects.

Future research will concentrate on the use of

Fuzzy Cognitive Maps (FCM) so as to approach

user behaviour from a quantitative point of view.

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(a) (b)

(c)

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