KEXPLORATOR: A 2D MAP EXPLORATION USER INTERFACE

FOR RECOMMENDER SYSTEMS

Gulden Uchyigit, Keith Clark and Damien Coullon

Department of Computing, Imperial College, London SW7 2BZ

Keywords:

Recommender Systems, User Interfaces, Map Exploration.

Abstract:

Recommender systems have reached some maturity and are getting more widely used with the rise of online

social networks. However, research until now was mostly focused on improving the recommendation engines,

without really advancing the way the recommendations were brought to users. This paper concentrates on

improving the delivery of recommendations to users via a new algorithm to allow for generation and 2D

visualisation of similarity networks with an emphasis on map stability. An implementation with a connection

to the Amazon recommendation engine has been developed.

1 INTRODUCTION

Recommender systems can be deﬁned as “any system

that guides the user in a personalised way through a

set of items”. The guidance can be explicit in the form

of personalised recommendations, but it can also take

the form of personalised browsing. Their domain of

application is very wide, ranging from the most obvi-

ous like culture discovery (books, music, movies) and

news personalisation to user segmentation and match

making or even group recommendation when a group

of people have to take a decision together, for exam-

ple which movie to watch as a group.

Recommender systems are divided into two main

categories: Content-based and Collaborative-based.

Content-based recommender systems (Pazzani and

Billsus, 1997), (Mladenic, 1996), (Balabanovic and

Shoham, 1995), (Armstrong et al., 1995) infer a user’s

proﬁle from the contents of the items the user has seen

and rated, and recommends additional items of inter-

est according to this proﬁle. In contrast, Collabora-

tive recommender systems (Kautz et al., 1997), (Her-

locker et al., 1999), (Goldberg et al., 1992), (Terveen

et al., 1997) make recommendations to the user by

collecting human judgements (known as ratings) for

items in a given domain and correlating people who

share the same information needs.

Over the past decade there has been a lot of de-

velopment in the area of recommender system algo-

rithms. But not much attention has been given to

the user interfaces of recommender systems (Sinha,

2002). Often recommendations are displayed to the

user as a simple ranked list. In this paper we present a

novel method of allowing users to explore recommen-

dations via an exploratory map, consisting of nodes

and arcs. The recommended items are the nodes and

the distance between the nodes, represented by the

arcs are the measure of similarity of the items. The

system uses a novel map placement algorithm. The

Kexplorator system is presented, which uses the new

map placement algorithm. The Kexplorator system

is a generic user interface system which can be used

with any recommender system engine. In this paper

we present Kexplorator working as a user interface to

Amazon’s recommendation engine.

2 OVERVIEW OF THE

KEXPLORATOR SYSTEM

The Kexplorator user interface (see Figure 1) is di-

vided into three parts the command centre, the map

viewer and the information centre. The command

centre consists of a control panel and a query zone.

The control panel allows for language selection and

item selection (e.g books, CDs, DVDs). The query

zone allows for query entry, such as entering a book ’s

223

Uchyigit G., Clark K. and Coullon D. (2007).

KEXPLORATOR: A 2D MAP EXPLORATION USER INTERFACE FOR RECOMMENDER SYSTEMS.

In Proceedings of the Ninth International Conference on Enterprise Information Systems - HCI, pages 223-228

DOI: 10.5220/0002365802230228

 SciTePress

title or an author’s name, to begin the exploration pro-

cess. The map viewer holds the current exploration

map. The information centre displays additional in-

formation on the item currently selected.

Command

Centre

Exploratory

Map

Additional

Information

Centre

Figure 1: The Kexplorator User Interface.

To begin the exploration process and to generate

the exploration map (see Figure 2), the user needs to

enter a search query. The most highly recommended

item to the user’s search query acts as the starting

point of the exploration process and will be placed

at the centre of the map, a similarity network is then

generated around this item.

Figure 2: Exploratory Map created for the item Orwell.

The user can explore the recommendations by

clicking on the items (nodes). This causes the map to

smoothly translate so that the clicked item moves to

the centre of the map. The similarity network around

the clicked item is updated and the map is expanded.

The items discovered appear slowly with a fade in.

Smooth transition when expanding the map is neces-

sary to preserve the mental map for the user (P.Eades,

1995), this is especially important for a visual rep-

resentation of recommendations. The choice of al-

lowing map translation only for the clicked items has

been made to allow the system to collect user’s brows-

ing behaviour, by inciting the user to click on items

of interest, the user’s browsing trail can be collected.

One of our future works is to use the users brows-

ing behaviour on the map. For instance the width of

the arcs can be used to represent the number of users

which followed that path. This information can assist

the user in browsing the items.

At any time, the user can load another map by sim-

ply restarting the process with a new starting point

query.

The nodes on the map are different colours and

sizes. The size and colour of nodes convey extra in-

formation to the user, regarding the popularity of the

item. For instance, smaller nodes indicate that the

items are less popular than the items represented by

larger nodes.

Additional information such as sales rank (popu-

larity), reviews, average user rating, is given on the

last clicked item, in the information box.

3 THE MAP GENERATION

ALGORITHM

The algorithm is divided into two parts the generation

of the neighbourhood and generation of the map.

3.1 Neighbourhood Generation

An item’s neighbourhood is the collection of the clos-

est items to it. By closest we mean those items which

are similar. Similarity can differ in different recom-

mender systems. For instance a recommender sys-

tem which uses user ratings for the recommendations

the similarity may represent the similarity of the rat-

ings of the items and in a recommender system which

utilises the content of the items the similarity may rep-

resent the similarity of the content of the items. To

this end the similarity is different for different recom-

mender systems.

The idea of locality in a map is crucial in which

humans perceive data, we don’t look at the whole map

from a centralised point, but rather by navigating from

one point to the next. In Kexplorator, the concept of

the item neighbourhood is designed to resemble this

behaviour. The item neighbourhood is the iterative

process of making a limited number of requests about

the surrounding items.

At the start of the exploration phase a similarity

network is generated which is generally a sparse ma-

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trix representing the similarity network for the start-

ing neighbourhood. Generation of the similarity net-

works is based on the idea of level browsing. At the

initial level there are the items which are similar to

the starting item, next level are the items which are

similar to those items and so on.

3.2 Transforming a Generic Similarity

Measure into a Visual Distance

To place points onto the map we need to transform

generic similarities into visual distances. To do this

we use our algorithm. The algorithm works provid-

ing the point to be placed has at least two links which

are already placed on the map. We compute the (x,y)

coordinates of point A (i.e the point to be placed) us-

ing the following formulae.

x =

∑

i∈Ω

∗ s(A, i))

∑

i∈Ω

(s(A, i))

y =

∑

i∈Ω

∗ s(A, i))

∑

i∈Ω

(s(A, i))

where, Ω is the set of the indices’s of the items

that are linked to the points that are already placed,

s(A, i) is the similarity of point A with point i, where

point i is already placed.

3.3 Optimising Arbitrary Item Selection

The order in which we select items has an impact on

the representation of the neighbourhood. A point can

be placed if:

• it has at least a link with two different points al-

ready placed, in which case, we use the algorithm

above to place the item.

• it has only one link to a single point, which is al-

ready placed and we place this point in the next

available arbitrary position near to the point al-

ready placed.

When we start the drawing of the map or whenever we

can’t place anymore items because of the sparseness

of the similarity matrix, we have to choose some items

to be placed arbitrarily. The simplest way of doing

this is by arbitrarily choosing the ﬁrst item not yet

placed.

Each time we place an item arbitrarily, we in-

troduce some distortion in the representation of the

neighbourhood. We would then like to minimise the

number of times we do this. One way, would be to

select the item with the highest number of direct links

for arbitrary placement. This technique should be re-

ﬁned by counting only the items not yet placed in the

number of direct links, since they are those who could

potentially cause us to select again an item to place

arbitrarily. Furthermore, we could only count links

to items not already placed that would become place-

able, which are the items that have at least one link

with one already placed item and items that have only

one link with the current item.

4 IMPLEMENTATION DETAILS

We used Flash 8.0 with ActionScript, for the imple-

mentation of the system. An emphasis was given to

the generality of the system, so that nearly any kind

of similarity measure and any recommender engine

could be connected to it. The ﬁrst choice was to de-

ﬁne an XML input interface. An XSLT style sheet

was designed to make sure the response of the rec-

ommender engine would conform itself to the pre-

vious schema. For this particular implementation,

we decided to make a connection to the Amazon E-

Commerce Web Service. There were a couple of other

possibilities for a connection with a recommendation

engine, like Last.fm, but Amazon had two key ad-

vantages: a proven history and support for this web-

service, as well as a huge database, available for many

kinds of items in different languages.

Once the similarity network is constructed, we

used the map placement algorithm, separating the

construction of the map in two phases: initial map

construction, then additional map redrawing. The

whole process was designed in order to be able to

switch from one recommendation engine to another

relatively quickly, as well as being able to add more

options during the implementation phase. We ﬁrst

tried to make a working proof with a simple “book

only, Amazon.com only” connection.

5 EVALUATIONS

The system was evaluated by users using the system

and answering a set of questions. The questions were

designed to assess the quality of the recommendations

received by the user, the efﬁciency of the system and

the overall look and feel of the system.

5.1 Recommendations

The goal of this part of the test was to assess the qual-

ity of the recommendations that the users received

whilst using the system.

A hefty majority of users managed to ﬁnd at least

one item that they didn’t previously experience, but

KEXPLORATOR - A 2D Map Exploration User Interface for Recommender Systems

225

that they liked (see Figure 3). The provision of such

recommendation is the goal of the system, and this

means that one of its objectives has been fulﬁlled.

Figure 3: Ability to ﬁnd useful recommendations, which

are items that they have not previously experienced, but that

they think they will probably appreciate.

Measuring the time needed by the user to get good

recommendations is important in order to assess its

efﬁciency (see Figure 4). With an average time of 1

minute, we can consider that the system is quite effec-

tive, although it might be improved on this point, for

example by accelerating familiarisation with the sys-

tem for new users. The efﬁciency is certainly better

for experienced users.

Figure 4: Time needed to ﬁnd one useful recommendation.

Figure 5 shows the time needed to ﬁnd three useful

recommendations.

This result is actually quite surprising. We didn’t

expect users to stop their search after just ﬁnding one

item, but some were satisﬁed with that. Here, the av-

erage time to discover three good recommendations

is 90 seconds for users that managed to ﬁnd at least

three items, which amounts for less than half of the

total number of users. This average time is good, but

the fact that it was reached by less than half of the

testers was quite disappointing.

Figure 5: Time needed to ﬁnd 3 useful recommendations.

Notice that many users were not even looking for 3 new

items.

5.2 Session

This section was focused on studying the behaviour

of users while using the system. The average dura-

tion of a session on Kexplorator was found to be over

6 minutes, with durations ranging between 1 and 15

minutes. This is inside the range of duration we were

expecting (see Figure 6).

Figure 6: Distribution on the number of items clicked per

session.

The number of items clicked is always higher than

one, which means that all the users quickly under-

stood the way they could navigate inside the map, and

felt the need to explore further. Most clicked on 4 to

10 different items, which is in line with our projec-

tions for short to average sessions. The sessions with

more than 20 clicks on items are the longest ones.

The average number of queries used is pretty dis-

appointing (see Figure 7), looking as though this fea-

ture was not completely exploited by our users. Only

50% of the users tried to make another query. For

those who tried to do more than one query, the aver-

age number of starting points tested is just over 3.5.

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Figure 7: Number of queries used by a user during one ses-

sion. The search feature seems to be under-used, and should

be enhanced for a more accessible use.

5.3 System

This part of the study was trying to evaluate the way

the system was perceived and understood by users.

We started by asking if the user thought he had under-

stood the meaning of the map, and veriﬁed his answer

by asking him to explain it explicitly. Then, we evalu-

ated his satisfaction: did he trust the system, would he

use it again, would he even be prepared to pay for it?

Finally, we asked the user to provide an overall rating,

using labels from bad to excellent to ensure the rating

system to be homogeneous.

Figure 8: Understanding of the meaning and principles of

the map. All the users thought they understood more or less

the way the map was functioning.

All the users were able to explain the meaning of

the map, and many of them were curious about the

way we generated the map. It might be an idea to

be more transparent on the actual algorithm used to

improve again trust in the system. This result on the

understanding of the map is a real satisfaction, being

a target difﬁcult to achieve for a complex and inno-

vative system. Most users were also able to point out

correctly the differences between item representations

and even managed to cite the criteria behind them

(popularity), which means that the interface chosen

is adapted to its audience (see Figure 9).

Figure 9: Perceived item differences. Rather accurate.

The system was unanimously perceived as useful

(see Figure 10).

Figure 10: Perceived usefulness of the system, along with

willingness to pay for it. Unfortunately, high usefulness is

not synonymous with willingness to pay.

On top of that, users had a high level of conﬁdence

in the system (see Figure 11) and even those who were

unable to ﬁnd new items for during the test session

asserted they would gladly use the system again on

another occasion.

Figure 11: User trust in the system, accompanied by inten-

tion to use the system again. Very positive indicators.

With all the previously mentioned satisfaction in-

dicators being very positive (see Figure 12), the av-

erage overall rating of 4.15 doesn’t really come as a

surprise, although it is unexpected for a system still

in beta version. It is very encouraging for the system

development.

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227

Figure 12: Overall ratings for the system, on a scale from 1

to 5. Very good results for a system still in beta version.

6 CONCLUSIONS

In this paper we presented the Kexplorator system,

which provides a generic map exploration user inter-

face for recommender engines. The system uses a

new algorithm for placing items on the map. Kex-

plorator has features such as on the ﬂy information on

items, improved colour usage (via continuous colour

gradation for popularity), and optimised size adapta-

tion.

Usability testing indicates that the map ex-

ploratory interface was an easy to use efﬁcient method

of allowing users to explore recommendations, and

discover other items of interest whilst browsing simi-

lar items.

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