Andreas Komninos
, Athanasios Plessas
, Vassilios Stefanis
and John Garofalakis
Mobile and Ubiquitous Computing Group, Glasgow Caledonian University, Cowcaddens Road, Glasgow, U.K.
Department of Computer Engineering and Informatics, University of Patras, Patras, Greece
Computer Technology Institute “Diophantus”, Rion, Patras, Greece
Keywords: Context awareness, Dimensionality Reduction, Mobile Personal Information Access.
Abstract: We propose an application of the Fastmap algorithm that could provide a breakthrough in the efforts to
present mobile personal information to the user in context, and describe our vision for context-driven
interfaces generated by this method that will support the richness of data stored in personal devices.
Advances in mobile hardware technology in terms of
storage space and data generation modalities
(efficiency-enhancing input UIs, sensor-generated
data and meta-data) allow today’s mobile device
user to quickly generate and store large volumes of
personal data. Much of this data is organized in
structured repositories (e.g. contact list, photo
gallery, message inbox), affording the user a means
of learnable, procedurised retrieval. Even though in
daily situations the user may require access to
multiple types of personal information, in most
devices the structured repositories remain mostly
“walled gardens”, requiring the user to sequentially
visit each of them in order to assemble the
information pieces she needs in one coherent
mentally held collection, relevant to their current
activity. Naturally, the cognitive load on the user
increases with the number of information items that
have to be retrieved from the repositories. But still,
even considering each “walled garden” individually,
it quickly becomes apparent that current retrieval
methods are largely inefficient, in view of the ever-
expanding storage space and richness of personal
information stored in mobile devices. For example
consider work on visualizing and retrieval from
large mobile photo galleries (Hsu et al., 2009) or
ever expanding music collections (Tolos, Tato and
Kemp, 2005) or contact lists. What can a user do
with all this data? Surely users want the data, but is
it really useful in its raw form as a singular item and
can it be used to help them achieve their goals?
Mobile devices such as smartphones, are still
primarily information access devices and
communication devices. Much of the activity on
mobile devices, especially mobile phones, is in
support of Human-to-Human interaction.
Consequently, many of the actions that someone
might perform on their mobile device involve the act
of looking up a contact, that perhaps carries at that
time some importance to the user, in order to initiate
some form of communication (call, sms, email,
comment on facebook etc.). Not forgetting that
communication is by definition the exchange of
information, information items are an important part
of it. Thus, mobile devices should support not just
the retrieval of contacts, but also of information that
is somehow relevant to these contacts. It is critical
here to underline that our work extends beyond the
notion of contact importance – in fact, we believe
that importance as a multidimensional vector can
provide a complex query “key” on the large, rich
dataset in a user’s mobile device for any type of
personal information item (e.g. a photograph, or a
set of SMS messages or stored Word documents) so
as to answer the question of who to communicate
with and what to communicate. As such our aim is
to construct an extensible, flexible approach to the
definition of importance in a k-dimensional context
space, which can be applied to any mobile
information retrieval problem (and consequently UI
design for mobile information access). We choose to
start with the concept of contacts as a problem
Komninos A., Plessas A., Stefanis V. and Garofalakis J..
DOI: 10.5220/0003688504850490
In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR-2011), pages 485-490
ISBN: 978-989-8425-79-9
2011 SCITEPRESS (Science and Technology Publications, Lda.)
domain, largely due to its importance in everyday
interaction with mobile devices.
Our motivation stems from the observation of user
behaviour in accessing non-contextualised
information from single repositories, namely the
mobile contact list. In recent work (Bergman et. al.,
2011), we demonstrated some interesting user
behaviours, which we believe, highlight the issue of
non-contextualisation. Firstly, we observed that our
18 young users (<25 yrs. old) from varied
backgrounds tend to keep fairly large repositories of
contacts (m=92.47 sd=56.93), which, as literature
suggests (Boardman and Sasse, 2004), are bound to
get bigger as they grow older (just one of our users
reported actively deleting unused contacts). In these
collections, a large percentage of contacts (av=39%,
sd=17%) has either not been used in over 6 months
or not at all. Viewing the collections as a whole
(n=754 contacts), over 47% of all contacts had not
been used in over 6 months or at any other point,
while just 15% were identified as frequently used
contacts, the remaining 38% having been used
between 1 and 6 months previous to our study
period. We also found a highly significant
correlation between the size of the contact list and
the number of unused contacts (r=0.81). In a
carefully designed experiment, we presented our
users with a contextualised UI which placed
“important” contacts in an alphabetic list, followed
by an alphabetic list of all other contacts and asked
them to perform several retrieval tasks on this
augmented UI and on a classic (Nokia) UI.
Importance in this experiment was set as a single-
value vector of frequency of use, with 6 months or
more being a threshold for considering a contact as
not important. We found that users provided very
positive subjective feedback to their experience
using this approach (86% found it easier to use than
the traditional UI and 64% would like to have this
feature on their next device). This was backed up by
significant performance metrics. On average users
required less button presses to successfully “find” a
contact (mean reduction=1.96, sd=3.56) and
significantly less time with the augmented UI
(m=4,424ms, sd=1,872ms) than the traditional UI
(m=5,204ms, sd=2,829ms).
While these findings assumed a naïve
classification of importance (just frequency of use),
they demonstrate quite effectively how
contextualisation on just a single dimension can
have a very positive effect on the retrievability of
personal mobile information. In the past, other
researchers have demonstrated either issues with the
usability of contact and call lists (Böcker and
Suwita, 1999) (Klockar et al., 2003) or improvement
in usability through the introduction of user activity,
proximity & state context (Oulasvirta et al., 2005),
social context (Gaur, 2008) (Rhee et al., 2006) and
temporal context (Jung et al., 2008). Further work by
Ankolekar et al. (Ankolekar et al., 2009) discusses
how a combination of contextual cues might offer
usability advantages but leaves the categorisation of
contacts to users and does not present any tangible
research into one of the most oft-used applications
of mobile devices. The volume of research in contact
list use remains low and the technology behind
contact list UIs in today’s devices is mainly based on
alphabetic lists. Currently, only the Android OS
provides a feature of presenting a contact list by use
frequency, though again this is a simplistic view of
importance, considering just one type of context (fig.
Figure 1: The Android contact list UI, augmented with a
single-dimension context cue (frequency of use) [left] and
a standard Symbian contact list UI, restricted to
alphabetically ordered lists [right].
Mobile devices collect a significant amount of data
and information about the user's context. Such
information includes location (absolute or relative),
the current time, whether the user of the device is on
move and their speed, the orientation of the device,
the user’s current task (e.g. on the phone,
messaging), whether the vibration or the silent mode
are enabled (Beach et al., 2010) etc. The user
considers her mobile device a "trusted device". She
KDIR 2011 - International Conference on Knowledge Discovery and Information Retrieval
usually has the device close to her, sometimes
operating 24 hours per day. Devices contain a lot of
personal information related to the user’s social
environment (Toninelli et al., 2008). These are often
generated automatically by the device (e.g. a
smartphone's phone list saves the calls that have
been made, the time of the day for each call and the
duration of each call for the past few days or even
weeks). Moreover, mobile devices store user-
generated content (e.g. SMS/MMS and audio files,
browser's history, calendar with user's events etc).
Therefore, a mobile device could also be aware of
the social environment of the user (social context).
In addition, mobile applications can take advantage
of social data from online social networks in order to
enrich a contact with more information (Bentley et
al., 2010). The combination of social and mobile
context results in a dynamically defined social
context, termed the mobile social context (Gilbert et
al., 2009). Therefore, a truly context aware mobile
information access application has to consider social
context as part of its context representation.
It is therefore quite apparent that true
contextualisation is much more complex than in our
previous experiment’s assumption, and that it
requires k-dimensional space in order to be defined.
As shown in equations (1), (2) and (3) we use a
vector model to represent the context of an
information item i in a k-dimensional space as a k-
tuple, where d
is the context atom value for
dimension n. Its importance can be considered as the
sum of the weighted distances between the vector
atoms describing current context properties and an
item’s context atoms (
21 n
dddiC =
)()()( iCnCiC
Researchers in the past have often attempted to
combine the user's input with the user's context in
order to provide a richer user experience (context-
aware applications), e.g. (Yoon et al., 2008). Most
approaches tend to focus on narrow objectives, as
the capture of context for general use is still
considered very difficult. In literature, context has
been represented in the form of vectors, e.g. (Du and
Wang, 2008) while other researchers have adopted
an ontological approach, e.g. (Korpipaa et al., 2004),
often using naïve Bayes classifiers to solving the
issue of defining context space. Vector based
approaches carry the disadvantage of computational
complexity in similarity searches (each dimension
needs to be compared separately) and that weighed
vectors require an empirical (hence error-prone or
narrowly applicable) estimation of the weights. On
the other hand, ontologies tend to be inflexible and
too strict to be applicable to wider ranges of
problems, requiring careful, manual approaches to
their construction.
In the previous section we outlined the
disadvantages of the vector and ontological
approaches for determining context. In our view, the
vector approach can offer a more flexible solution to
context acquisition and representation, hence our
work relates to overcoming its computational
complexity and vector weighting issues. In
(Komninos and Liarokapis, 2009) we proposed four
criteria for the determination of a m-PIM (mobile
Personal Information Management) item importance
(namely contacts). From these criteria, we can derive
the following context dimensions for the contact list
problem domain, on which a contact can be mapped:
Frequency (e.g. Frequency of use in last n
Recency (e.g. days since last use)
Location (e.g. Geographic areas from which at
least n% of uses are made)
Time of Day (e.g. Time segment of day in
which at least n% of uses are made)
Task (e.g. Boolean measure of existence of a
scheduled task involving a contact within a
certain window of time [for example today] or
temporal distance between now and such
scheduled task?)
Personal preference (e.g. scale of 1-5 of explicit
user rating of importance for a given contact)
In order to estimate a “match” signifying
importance between these types of contextual
information and the user’s current context, a typical
approach would be to measure the distance between
current context and contextual data (e.g. time of day
now vs. usual time of day of contact use) and
combine this with static context (e.g. explicit
importance rating). The derived metrics would need
to be weighted and the sum of these weighted
metrics could then be used to infer “importance” for
a single contact under any context (equations (1), (2)
and (3)). This approach though is not without
challenges: Firstly, one must determine appropriate
weights for each context type. Subsequently, it is
easy to realize that this would be a futile attempt, as
the weights of each context type are naturally
dynamic and can vary under different use contexts
(see scenario in figure 2). The example scenario
introduces the problem of context-derived specific
importance (vs. general importance) and shows that
a decent approximation to the calculation of this
term is very difficult, due to the infinite variability
of context itself and the complexity of its
dimensions. A possible solution however could
come from the field of Databases and IR, where
dimensionality reduction is a technique often used to
automatically extract important features from
complex data and reduce the complexity of searches
in multidimensional spaces.
Take the example of “John”, a contact that the user
calls every day and “Jane” another contact that is only
called once a year. “John” can be considered generally
important. However if the user is running late for a
meeting with Jane Doe that will take place in 10
minutes, then Jane becomes undeniably more
important than anyone else, and her importance should
rise and decay naturally as time flows around the
scheduled event.
Figure 2: The Importance Scenario.
DR, as the name suggests, is an algorithmic
technique for reducing the dimensionality of data,
applied in several computer science fields such as
databases, information retrieval, data mining,
recommendation systems, signal processing etc.
Real-world data usually has a high dimensionality, a
fact that affects data processing performance (the so
called “curse of dimensionality” originally appearing
in (Bellman, 1957) that suggests exponential
dependence of an algorithm on the dimension of the
input). The idea is to transform data from a high-
dimensional space to a low-dimensional space,
preserving some critical relationships among
elements of the data set. In mathematical terms,
given a p-dimensional object x=(x
, find a
lower dimensional representation of it, s=(s
with kp, that captures the content in the original
data, according to some criterion (Fodor, 1992).
There are two categories of methods in order to
solve the problem: a) feature selection, where an
optimal subset of features (dimensions) is chosen
and b) feature extraction, where existing features are
combined and transformed to new ones.
Our research idea is to perform DR to context
augmented personal information items, such as
entries in a contact list, an idea that has not yet been
proposed and applied in scientific literature as far as
we know. Feature selection for context DR is not
practical, as it is neither possible to know the best-
describing features of the context vector nor their
weights in advance. Since, as already presented,
context augmented personal information items can
be represented as multidimensional vectors, we find
it highly appealing to try to extract a small number
of features that could accurately represent the
original items and their relationships, so as to enable
quick and accurate similarity searches for related
personal information items. Furthermore, after
reducing the dimensions of the items, it might be
desirable to map them to a 1-d, 2-d or 3-d space, as
often done in high-dimensional data projections,
since visualization tends to reveal existing groups of
FastMap algorithm:
1. Find two objects that are far away.
2. Project all points on the line the two objects
define, to get the first coordinate.
3. Project all objects on a hyperplane
perpendicular to the line the two objects
4. Repeat k-1 times
Figure 3: The FastMap Algorithm.
There is a wide range of algorithms with diverse
characteristics that achieve dimensionality reduction,
following different approaches. An interesting
method that could be appropriate for the case of
mobile phones due to its simplicity and
computational efficiency is the FastMap algorithm
(Faloutsos and Lin, 1995). FastMap is a fast
algorithm that maps high-dimensional objects into
lower-dimensional spaces, while preserving well
distances between objects and the structure of the
data set, as a result preserving also dis-similarities
between objects. Experiments presented in
(Faloutsos and Lin, 1995) show that the algorithm
performs well for visualization and clustering. The
algorithm functions as shown in figure 3 and its
complexity is O(Nk
), where k are the dimensions of
the target space. A further advantage of this
approach is that context vector “atoms” need to be
defined by application developers just once – the
recursive and atom-agnostic nature of the algorithm
allow it to work for any context description vector.
KDIR 2011 - International Conference on Knowledge Discovery and Information Retrieval
In the previous sections, we underlined the
significant role that mobile social context can play in
retrieving and presenting information to the user
from repositories within the mobile device that may
contain large volumes of data. We have proposed the
use of a vector model for the representation of
context and since finding weights (that may change
dynamically) is a very complex task, we introduced
the idea to apply the technique of dimensionality
reduction. This technique is characterised by its
ability to automatically produce meaningful clusters
of related information and thus can make the
contextualised visualization of personal information
items in 2 or 3 dimensions feasible.
We believe that our technique as described
above can be very useful in order to build rich
singular, two or three-dimensional information
retrieval interfaces that will support data from
multiple information repositories and present them
in context, as demonstrated in the mock-ups
presented in this paper (figures 4 & 5).
Figure 4: Hybrid one-dimensional mapping of importance
(important items are highlighted in bold but ordered
alphabetically so as to maintain user familiarity with
existing UIs).
In figure 4 a one-dimensional hybrid interface is
presented, where contacts are sorted alphabetically,
but for each letter the important contacts are on top
of the list and the remaining follow in alphabetical
order. In figure 5 we show some concept renderings
of 2-d and 3-d retrieval interfaces. In the case of 2
dimensions, the dimension of time can be preserved
and as a result the user is able to retrieve the most
important contacts during each time period. Finally,
in the case of 3 dimensions an example of how this
technique extends beyond the domain of contact lists
is presented. The respective figure (5b) shows how
several personal information items (contacts, e-
mails, SMSs etc.) could be projected in a 3-
dimensional space (with possible dimensions
presented on the axes of time, importance and
distance from current location).
Figure 5a (left) and 5b (right): Retrieval UIs using 2D
(left) and 3D (right) projections of item (contacts, e-mails,
photos etc.) importance combined with retained,
unprojected dimensions (time, distance from current
At this point in time our work focuses on a
context-enabled contact list and following trials will
extend to support a richer information space that will
include all types of media and information pertinent
to those contacts, enabling a new mode of context-
based search and retrieval for mobile devices.
This work has in part been supported by the National
Strategic Reference Framework (NSRF) (Regional
Operational Programme – Western Greece) under
the title “Advanced Systems and Services over
Wireless and Mobile Networks” (number 312179).
Ankolekar, A., Szabo, G., Luon, Y., Huberman, B. A.,
Wilkinson, D., Wu, F., 2009. Friendlee: a mobile
application for your social life. In Proceedings of
MobileHCI '09, Bonn, Germany.
Beach, A., Gartrell, M., Xing, X., Han, R., Lv, Q., Mishra,
S., Seada, K., 2010. Fusing Mobile, Sensor, and Social
Data To Fully Enable Context-Aware Computing. In
Proceedings of HotMobile 2010, Annapolis,
Maryland, USA.
Bellman, R., 1957. Dynamic Programming, Princeton
University Press. Princeton, NJ, USA.
Bentley, F., Kames, J., Ahmed, R., Zivin, R.,
Schwendimann, L., 2010. Contacts 3.0: bringing
together research and design teams to reinvent the
phonebook. In Proceedings of the 28th of the
international conference extended abstracts on Human
factors in computing systems, Atlanta, USA
Bergman O., Komninos A., Liarokapis D., Clarke J., 2011.
"You never call: Demoting unused contacts on mobile
phones using DMTR", Personal & Ubiquitous
Computing, Online First, DOI 10.1007/s00779-011-
Boardman, R., Sasse, M. A., 2004. "Stuff goes into the
computer and doesn't come out": a cross-tool study of
personal information management. In Proceedings of
CHI'04, Vienna, Austria.
Böcker, M., Suwita, A., 1999. Evaluating the Siemens
C10 mobile phone – Beyond “Quick and Dirty”
Usability Testing. In Proceedings of HFT’99,
Copenhagen, Denmark.
Du, W., Wang, L., 2008. Context-aware application
programming for mobile devices. In Proceedings of
C3S2E Conference, Montreal, Canada.
Faloutsos, C., Lin, K. I., 1995. Fastmap: A Fast Algorithm
for Indexing, Data-Mining and Visualization of
Traditional and Multimedia Datasets. In Proceedings
of ACM SIGMOD '95, ACM, New York, USA.
Fodor, I. K., 1992. A survey of dimension reduction
techniques. LLNL Technical Report, UCRL-ID-
Hsu, S.-H., Cubauld, P., Jumpertz, S. 2009. Phorigami: A
Photo Browser Based on Meta-categorization and
Origami Visualization. Human-Computer Interaction:
Novel Interaction Methods and Techniques Lecture
Notes in Computer Science, Volume 5611/2009, 801-
810, Springer.
Gaur, S., 2008. Mobile Phone Contact. In: Ylä-Jääski A,
Takkinen L (eds) Technical Reports in Computer
Science and Engineering.
Gilbert, P., Cuervo, E., Cox, L., 2009. Experimenting in
Mobile Social Contexts Using JellyNets. In
Proceedings of HotMobile 2009, Santa Cruz, USA.
Jung, Y., Anttila, A., Blom, J., 2008. Designing for the
evolution of mobile contacts application. In
Proceedings of MobileHCI'08, Amsterdam,
Klockar, T., Carr, D., Hedman, A., Johansson, T.,
Bengtsson, F., 2003 Usability of Mobile Phones. In
the 19th International Symposium on Human Factors
in Telecommunications, Berlin, Germany.
Komninos, A., Liarokapis, D., 2009 The Use of Mobile
Contact List applications and a Context-Oriented
Framework to Support their Design. In Proceedings of
MobileHCI'09, Bonn, Germany.
Korpipaa, P., Hakkila, J., Kela, J., Ronkainen, S., Kansala,
I., 2004 Utilising context ontology in mobile device
application personalisation. In Proceedings of
MUM'04, ACM, New York, USA.
Oulasvirta, A., Raento, M., Tiitta, S., 2005.
ContextContacts: re-designing SmartPhone's contact
book to support mobile awareness and collaboration.
In Proceedings of MobileHCI'05, Salzburg, Austria.
Rhee, Y., Kim, J., Chung, A., 2006. Your phone
automatically caches your life. In ACM Interactions
13, 4.
Tolos, M., Tato, R., Kemp, T., 2005. Mood-based
navigation through large collections of musical data.
Consumer Communications and Networking
Conference, 2005.
Toninelli, A., Khushraj, D., Lassila, O., Montanari, R.,
2008. Towards Socially Aware Mobile Phones, In
Proceedings of SDoW 2008, Karlsruhe, Germany.
Yoon, Y., Ahn, Y., Lee, G., Hong, S., Kim, M., 2008.
Context-Aware Photo Selection for Promoting Photo
Consumption on a Mobile Phone. In Proceedings of
MobileHCI’08, Amsterdam, Netherlands.
KDIR 2011 - International Conference on Knowledge Discovery and Information Retrieval