USER MODELLING SERVER FOR MOBILE

AND ELECTRONIC SHOPPING

Anastasios Savvopoulos and Maria Virvou

Department of Informatics, University of Piraeus, Karaoli & Dimitriou St, Piraeus, Greece

Keywords: User modelling server, e-Commerce, m-Commerce, Adaptivity, Clustering, Stereotypes.

Abstract: Recommending applications are used by many researchers as the main means of achieving personalization.

However, the difficulty is to apply the same recommender and personalization techniques to a totally

different system that belongs to a different domain and uses a different medium. Furthermore the difficulty

level rises when we want to apply the previous recommendation techniques without changing major

components or process attributes. In this paper we propose a server that can be used to achieve

personalization to a product recommending system. The main advantage of this server is that works both for

e-commerce and mobile commerce. We present a case study that we incorporated the user modelling server,

which is a mobile e-shop, and discuss within the case the obstacles and breakthroughs we have achieved.

The case study clearly shows that the same user modelling server can enhance e-shop application behaviour

towards adaptivity and customer personalization.

1 INTRODUCTION

e-Commerce applications have become very popular

since they provide easy access to all kinds of

products. However, most of existing applications are

generic and do not address specific needs,

preferences and attributes of individual customers. A

remedy to this problem can be achieved by web

personalization techniques. As De Pessemier et.al.

(Pessemier et.al. 2008) suggests, new technologies

such as Internet, iDTV, and mobile applications

create the possibility to advertise in a different, more

attractive manner than the traditional commercial

breaks. This leads to a rise of interactive

commercials on websites, banners on the internet,

and commercials on mobile devices. Thus the

market moves towards more converged architectures

corresponding to different types of mediums such as

mobile phones, internet. A solution to convergence

can be found within user modelling servers (Kobsa,

2001). In this paper we will propose a user

modelling server for e-commerce and m-commerce

applications that incorporates a clustering algorithm

in order to create groups of similar users, concerning

their needs and interests. The paper is consisted of

five sections: Introduction, Related Work, User

Modelling Server Architecture and the Case Study.

2 RELATED WORK

There are many e-shop applications that try to make

recommendations using many techniques. These

techniques usually involve the construction of user

models that are either based on explicit user

information or on data about the user behaviour that

is collected implicitly by the system. User models

can be constructed using many techniques like

stereotypes (Rich 1979), adaptive hypermedia

(Brusilovsky, 2001) or individual user models.

Another approach that goes beyond attributes

exploitation like the work mentioned previously is

done by J. Alspector et.al. (Alspector et.al. 1997).

This approach builds a CART network with the help

of three main adaptive techniques: feature-based,

clique-based and linear model. Their study showed

that an effective movie-recommendation system

should combine all these approaches in order to

maximize performance. A very interesting technique

also, based on a rating system has been conducted

by Q. Li and B. M. Kim (Li & Kim, 2004). The

system acquires rates and then calculates fuzzy

inferences and extracts similarities between users.

Their method proved very successful according to

the evaluation presented. Another recommending

system that uses clustering to provide

recommendations has been made by Ardissono and

Torasso (Ardissono & Torasso, 2000). In their

297

Savvopoulos A. and Virvou M. (2009).

USER MODELLING SERVER FOR MOBILE AND ELECTRONIC SHOPPING.

In Proceedings of the 4th International Conference on Software and Data Technologies, pages 297-302

DOI: 10.5220/0002243202970302

 SciTePress

research they created a web-store shell using

dynamic user models.

In the field of mobile-shopping little work has

been done. An interesting approach has been made

by Billsus et al (Billsus et al 2002). In their work

they have created and adaptive interface for mobile

devices that makes personalised suggestions based

on search questionnaires. Despite that fact that their

work can be very efficient, it mainly focuses on

filtered lists of suggestions on various areas such as

restaurants, presented to the user’s mobile device.

Their adaptive user interface lacks the generality of

a user modelling server that can be applied in both

internet and mobile shopping applications.

Despite the fact that all the above systems provide

users with recommendations, they are so domain and

problem depended that lose the ability to be applied

with easy in different media or products. In this way

every time a new application is built a new

architecture must be constructed from scratch in

order to address the specific application’s problems.

Few systems have tried to create user modelling

servers that can be applied to any product and help

users in an adaptive way. A very important research

on this field has been made by Brusilovsky et al.

(2005). Their research is focused on user modeling

and adaptation in distributed E-Learning systems.

They describe CUMULATE, a generic student

modeling server developed for a distributed E-

Learning architecture, KnowledgeTree. They also

introduce a specific, topic-based knowledge

modelling approach which has been implemented as

an inference agent in CUMULATE and used in

QuizGuide, an adaptive system that helps students

select the most relevant self-assessment quizzes. We

also discuss their attempts to evaluate this multi-

level student modeling. On the other hand our user

modelling server applies to entirely different

domain. Its role is not distribute student models but

create adaptive customer models in real time as the

customer navigates through the specific application.

Another significant work towards convergence

has been done ns Personis by Kay et al. (Kay et al.,

2002). Personis is a server for user models that every

user can control his/her personal user model. Their

user model server gives every application its

monitoring a different view of the user model

database. This structure of this database is based on

objects. Our system has a user model server but

instead of just control it creates user models

dynamically. Our user modelling server provides

components that help systems create

recommendations and effectively classify new

customers without any prior knowledge of their

tastes.

In this paper we propose the main architecture of

a server that can be applied in any product

recommending application in order to transform it

into an intelligent application. This framework is

product and media independent. The main advantage

is that it can be applied on any product

recommending application without consideration of

the products used such as personal computers,

mobile phones or even cars. The architecture of the

user modelling server includes user modelling

techniques, intelligent user interfaces and clustering

algorithms in order to produce recommendations.

We also propose the basic steps on how to build the

user modelling server and present two case studies

in which we incorporated our server. The case

studies sell movies. The first one is an e-shop

applications and the second a mobile shop

application. The incorporation of our user modelling

server on both cases proves that this server can be

easily applied on any product recommending

system.

3 THE PROPOSED USER

MODELLING SERVER

ARCHITECTURE

The architecture of the user modelling server follows

the logic of “two modules” (figure 1). The first

module consists of components that perform

reasoning about users’ actions and users’

preferences (indirect components). More specifically

the first module consists of the following

components:

Explicit Information User Profiles: information in

a database about users provided to the system in an

explicit way (answering interview questions or

rating products). Every time a new user is registered

in the application that incorporates the user

modelling server, this component collects this

information from demographic data, educational

data and interest data that the user provides through

the registration process

Monitoring Agent: includes information about

user’s interactions with the system. Monitoring in

real time user actions throughout system usage. This

component contains information about the main

product categories that this user has visited, specific

products that user visited, products that user moved

in or out of his cart and products that user bought

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from the shopping cart. In this way this component,

is product independent because it monitors user

behaviour and not products characteristics. The

Monitoring Agent component also contains a

statistics database of all users’ actions and features

collected by the systems.

Clustering Algorithm Process: uses k-means

clustering algorithm in order to group similar users.

These groups are then processed and result in users’

representatives that the user modelling server can

use as the basis of users’ interests. The k-means

clustering algorithm takes as input the statistical data

of all the explicit and implicit information that the

system infers and collects about users. This data is

saved on the statistics database of the Monitoring

Agent. This data include the visits in products

specific pages, products general categories, search

queries made by the user, products moved to the

shopping cart and products eventually bought. Every

category and product characteristic is a calculated by

the algorithm. However, this calculation is not based

on degrees from products classification; instead the

calculation is based on user behaviour while s/he

interacts with the system. The data input for the k-

means algorithm are calculated based on degrees

from the features measured. An example general

equation can be seen below. This equation measures

interest degree in product category. Weights change,

depending on the application that this framework is

incorporated. However, general rules on weights

apply on all applications that use this framework.

For example, the weight concerning visits on a

specific product category is always smaller then the

corresponding weight of products moved to the cart.

The first weight is smaller, because visiting a

product may not mean that the user is necessarily

interested in this product but that s/he is just

browsing several products.

The equations above can be specialised to every

product the application that incorporates the user

modelling server. In this way user modelling server

remains product independent. The k-means

clustering algorithm processes these degrees and

provides the system with groups based on similarity.

From these groups representative feature vectors are

extracted. The representatives’ vectors work as

group leaders and show the groups tendency to

specific product features. These vectors are then

compared with the vectors of products’

characteristics and the closest vectors are extracted

and saved to the Recommendations Database

component. In this way the Recommendation

Database component is updated dynamically as

users navigate through the application.

oductsP

VisitsInAl

ygToCategoroductBelonPrVisitsIn

tegoryterestInCaDegreeOfIn

(1)

edBasketoductsPlacP

tegoryBelongToCaedInBasketoductsPlacPr

tegoryterestInCaDegreeOfIn

(2)

oductsPrAllBought

CategoryhtBelongTooductsBougPr

tegoryterestInCaDegreeOfIn

(3)

tegoryterestInCaDegreeOfIn*W

mpanyterestInCoDegreeOfIn

(4)

Double Stereotypes: dynamic double stereotypes

from these representatives. Stereotypes, within the

component, follow the rule “general to specific”.

These stereotypes form a hierarchy from top to

bottom. This means a hierarchy is constructed with a

low number of generic stereotypes at the top. As the

hierarchy continues to bottom the stereotypes

participating in the same level are constructed with

more specific attributes. This hierarchy is continued

until it reaches a certain point of complexity.

Server: The Server component is involved in the

manipulation of all the information about a specific

user, the stereotype that s/he belongs to and his/her

more similar representatives. The Server also

contains all users’ models and manages them in

order to give the right information to the

components of the second module. In this way the

server acts as a service of communication between

indirect and direct components.

The second module consists of user interface

components (direct components).

Incremental Initialization Process: acquires

information from the server and provides the closest

recommendations to new users or users that the

system has little information about. Stereotypic

information is used throughout this process, from the

Double Stereotypes component, to fill the blanks of

knowledge concerning the specific user. The main

operation of this process is to choose whether a user,

according to the actions made so farm, belongs to a

group or not.

Recommendations: communicates with the Server

component and provides the users with personalised

USER MODELLING SERVER FOR MOBILE AND ELECTRONIC SHOPPING

299

Figure 1: User Modelling Server General Architecture.

recommendations and advice. The Recommendation

component is a database with system

recommendations about products, mistakes or other

personalised advice in a database. This main

techniques used by this component are adaptive

hypermedia and dynamic annotations. The

Recommendation’s database is built by the user

modelling server adaptively in real time as the user

provides the system with feedback along his/her

interaction with the system.

User Interface: adjusts to the medium used

automatically and changes according to the users’

interests. The whole user interface appearance

changes adaptively and personalises its behaviour

according to the specific users’ inferences and

needs. The User Interface is an entirely separate

component and adapts to any medium thus making

the user modelling server medium independent.

These two functions of the User Interface

component create a unique personalised experience

for every specific user, resulting in a friendlier and

more efficient user interface. Except the Server this

component communicates with the

Recommendation and Incremental Initialization

components in order to adapt the user interface

accordingly.

Adaptive Hypermedia: component with product

annotation ability and dynamic placing of

recommended products. This component has the

ability change the symbols of recommended

products depending on the degree of the

recommendation. For example, highly recommended

products by the user modelling server are tagged

with a different symbol by this component. On the

other hand, lower recommendation degrees can

result in different annotation product symbols.

Application fonts and font-sizes can also be changed

by this component in order to get user’s attention

and differ his/her behaviour. For example, if a user

is greatly interest in a specific product feature then

in adaptive hypermedia will enlarge the font of this

feature in the product page, giving it the proper

attention.

4 THE CASE STUDY MVISION

Our case study is a mobile shop that sells movies,

called mVision. MVision is built on ASP.Net

Mobile uses the mobile device resources of every

customer to create an effective user interface for

every customer. Figure 2 shows the main movies

categories page. Next from the action-adventure

movies category we have the implementation of the

adaptive hypermedia technology with the symbol

. At the top left of the screen is the “cart”

button that can be pressed to access the specific

customer’s cart. MVision is corresponding mobile

shop of Vision.Com (Virvou et al., 2007).

For every customer mVision creates a different

record at the database. There are two types of

information saved for every user, the explicit and

implicit. The explicit information is saved on the

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Explicit Information User Profiles and the implicit

are saved by the Monitoring Agent. In mVision

every customer can visit a large number of movies

by navigating through four movie categories. These

four movie categories are: social, action, thriller and

comedy movies. All customers have their own

personal shopping cart. If a customer intends to buy

a movie she/he must simply move the movie into

her/his cart by pressing the specific button. After

deciding which movies to buy a customer can easily

purchase them by pressing the button “buy now!”

All navigational moves of a customer are

recorded by the system in the statistics database by

the Monitoring Agent. In this way mVision saves

statistics considering the visits in the different

categories of movies and movies individually. The

same type of statistics was saved for every customer

and every movie that was moved to the buyers’ cart.

The same task is conducted for the movies that are

eventually bought by every customer. All of these

statistical results are moderated from one to zero and

saved in the statistics database. In particular,

mVision interprets users’ actions in a way that

results in the calculation of users’ interests in

individual movies and movie categories. Each user’s

action contributes to the individual user model by

implying degrees of interest into one or another

movie category or individual movie. For example,

the visit of a user into a movie shows interest of this

user to the particular movie and its category. If the

user puts this movie into the shopping cart this

shows more interest in the particular movie and its

category. If the user buys this movie then this shows

even more interest whereas if the user takes it out of

the shopping cart before payment then there is not

any increase in the interest counter. Apart from

movie categories that are already presented, other

movie features that are taken into consideration by

mVision are the following: price range, leading

actor, director and price.

As a consequence, every customer’s interest in

one of the above features is recorded as a percentage

of his/her visits in movie-pages similarly to

Vision.Com. MVision like Vision.Com incorporates

the AIN clustering algorithm. In mVision we also

used the AIN representatives in order create double

stereotypes that are saved in the Double Stereotypes

component of our general architecture. The

stereotypes concern both users and movies. Again,

levels 1 are the more general stereotypes and level 5

the more complex ones. The more information the

system has about a user the more complex

stereotypes it uses, until it reaches the level 5. After

level 5 the system uses an individual user model

about this user. Double Stereotypes and individual

user models use the Adaptive Hypermedia

component to suggest movies (figure 3). Figure 3

shows the implementation of the adaptive

hypermedia technology in the recommendations

page. In this example this specific customer,

according to his/her user model, likes these movies

very much, so the user modelling server provides

mVision with the right information and degrees of

interest in order for mVision to be able to make

these movies suggestions. The symbols of

and

show greater interest degree opposed to the

symbol. MVision does not support an animated

agent due to hardware limitations.

Figure 2: mVision User Interface from Movie Categories

page.

Figure 3: Recommendations Page.

5 CONCLUSIONS

In this paper we proposed a user modelling server

that can be used to achieve personalization to an e-

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301

commerce system. The main advantage of the user

modelling server is that it can be incorporated as a

component in different media such web or mobile

systems. We presented a case study that we

incorporated this user modelling server. The case

study was on a mobile device e-shop application.

This case study proves the independency of our user

modelling server and how easily it can be

incorporated to any kind of shopping system. Our

case study also showed how the component can

change the application’s user interface in order to

benefit from the adaptivity and suggest products

more efficiently.

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