![](bg4.png)
However, when we consider only the differences in
the order (Figure 3) we have, on average a
difference under 1, which indicates that it is, in
general, in accordance with the user’s preferences.
In all the scenarios considered, the Adaptive DOI
had better results than the Standard DOI, obtaining
both values and order that better matched the values
indicated by the users. This confirms H5; however,
surprisingly H4 is disproved since even for users
with no difference in preference, the Adaptive DOI
had better results. In fact, the results obtained with
the different groups of users do not shown a
significant difference between them.
Figure 3: DOI vs. ADOI: average order differences.
To validate H1, we examined how many users
chose to perform their query of task 4 with the
Exploratory Mode. Despite the slightly worse
usefulness classification, only two participants used
other DOI modes, thus validating H1.
Concerning H2, the hypothesis is only partially
validated. While in task 5, two thirds of the
participants preferred to use the Adaptive DOI,
when asked which one they would prefer, we
obtained mixed responses, with an equal number of
users preferring each mode. Instead, more than half
the users prefer to have both functions available.
This is, in part, contrary to what we would suppose,
since the Adaptive mode consistently obtains results
that better match the user’s classifications.
Finally, regarding H3, our results partially
contradict our hypothesis. Despite being, in general
more used than the geographical distance, when we
asked the participants which one they would prefer,
we had twice as many participants choosing the
geographical distance. It should, however, be
stressed that, more than half the participants would
prefer to have both distances available.
4 CONCLUSIONS AND FUTURE
WORK
Our work provides evidence that user preferences
change, sometimes significantly, depending on the
context in which they are (both temporal and
geographical).
We can also clearly witness an improvement in
both the values and the order of the POIs when using
the adaptive DOI. This improvement suggests that
the use of richer contextual information can
significantly improve the way applications model
and identify the user interests, enabling a better
selection of the information presented to the user
and its relevance. By having a better judgment on
the choice of presented information, and displaying
it in a way that more closely resembles the frame of
mind of the user, we can considerably reduce the
cognitive load associated with these systems and
increase their usability.
We also witnessed some classifications by the
users that raised interesting questions. For example,
one of the users classified a restaurant with 0%,
because the Type was vegetarian, and the user really
disliked that type of food. This hints that, possibly,
there should not only exist positive preferences, but
also, negative ones.
Regarding future work, we intend to explore a
number of different contexts that could also be used
to further filter and partition the Historical Context
database. The use of the current climate conditions
in the area of the user, for example, can alter the
preference for restaurants with or without a seafront,
depending on whether it is raining or not. Similarly,
when the users were considering the vacation
scenario in a different country, many expressed the
desire to choose the restaurant type as “typical”.
This indicates that the notion of being abroad (easily
found by analyzing the user coordinates) can also
significantly alter the user preferences.
REFERENCES
Adomavicius, G., Kwon, Y., 2007. New Recommendation
Techniques for Multi-Criteria Rating Systems. In
Intelligent Systems, IEEE, 22 (3), 48-55.
Adomavicius, G., Mobasher, B., Ricci, F., Tuzhilin, A.,
2011. Context-Aware Recommender Systems. In AI
Magazine, 32 (3), 67-80.
Carmo, M. B., Afonso, A. P., Pombinho, P., Vaz, A.,
2008. Visualization of Geographic Query Results for
Small Screen Devices. In Proc. of the Visual 2008,
LNCS 5188, 167-178.
Heimonen, T., 2002. Information Visualization on Small
Display Devices. Master Thesis. Department of
Computer Sciences, University of Tampere.
Holtzblatt, K., 2005. Designing for the Mobile Device:
Experiences, Challenges and Methods. In
Communications of the ACM, 48 (7), 33-35.
0,83
0,85
0,80
0,57
0,55
0,61
0,0
0,5
1,0
Total WithPref. WithoutPref
OrderDifferences(AbsoluteAverage)
Standard
DOI
Adaptive
DOI
IVAPP2013-InternationalConferenceonInformationVisualizationTheoryandApplications
540