(1) Mobile devices can now collect vast amounts of contextual data through sensors and user inputs. This data has potential to support applications in recommender systems, social psychology research, and transport information systems. (2) Research has shown that contextual data like location history and time/place attributes can improve predictions of a user's next location by up to 94% accuracy. However, privacy and data collection costs must be considered. (3) Ongoing work aims to develop adaptive sensor sampling to reduce energy and data transmission costs while still enabling context-aware applications and crowd-sourcing of mobility and transportation data.

1. mobile “context-awareness”
@neal_lathia
computer laboratory, university of cambridge
mobcom workshop

2. “by 2025, when most of today’s
psychology undergraduates will be
in their mid-30s, more than 5
billion people on our planet will be
using ultra-broadband, sensor-rich
smartphones far beyond the
abilities of today’s iPhones,
Androids, and Blackberries.”
Miller

3. how can we leverage the
data that they capture?
what applications can this
data support?
my research:

4. interaction device
challenges
inference

5. using data collecting
data
data challenges
learning
from data

6. “mobile devices and the mobile internet
represent an extremely challenging
search environment. Limited screen
space, restricted text-input and
interactivity and impatient users all
conspire...”
Church & Smyth

7. “...data harvesting through mobile
phones still presents a variety of
challenges […] energy consumption is
still very high for both data transmission
and resource-intensive local
computation...”
Rachuri et al. (2011)

8. modern-day applications do not take full
advantage of devices' potential
capabilities
as a result,
but still,...

9. foursquare

11. apps lack “context”
i try to avoid this word.

12. why? well, what is “context?”
time, place, intent, weather, social setting, mood,
product sales, application, interaction device…

13. “...decision making, rather than
being invariant, is contingent on
the context...”
Adomavicius & Tuzhilin
user perspective

14. “...the context of the user […] is
defned as the co-located
Bluetooth devices...”
Rachuri et al. (2013)
systems perspective

15. “... it is diffcult to fnd a relevant
defnition satisfying in any
discipline.”
Bazire & Brezillon

16. (1: learning) recommender systems
(2: collecting) social psychology research
(3: using) transport information systems
what would we like to support?

17. (1) recommender systems
users items
decision support in various domains

18. (1) recommender systems
users items
decision support in various domains
have preferences have features
context
somewhere here?

19. (1) recommender systems
(mobile)
users
locations,
events
decision support in various domains

20. Quercia et al. 2010

22. location + time + place features +
social network + history + likes
foursquare check-in:
data:
35,000,000 check-ins from 925,000 users
we ask:
(a) where will users go next? (b) what new
places will users visit?
context-augmented?

23. (a) where will users go next?
Noulas, Scellato, Lathia, Mascolo (2012)
we examine the performance of a variety of features encoded
in the mobile check-in, and their ability to accurately rank the
next place a user will go to.
we measure ranking quality using the Average Percentile
Rank, where: 0 = terrible, 1 = perfect.

24. (a) where will users go next?
Noulas et al. (2012)
random 0.5
user history 0.68
categorical preference 0.84
social fltering 0.61
popular places 0.86
geographically close 0.78
category hour 0.56
category day 0.57
place hour 0.76
place day 0.79
decision tree 0.94

25. (a) where will users go next?
Noulas et al. (2012a)
random 0.5
user history 0.68
categorical preference 0.84
social fltering 0.61
popular places 0.86
geographically close 0.78
category hour 0.56
category day 0.57
place hour 0.76
place day 0.79
decision tree 0.94

26. why consider each check-in in isolation?
follow-up work:
Noulas et al. (2012b)

27. (1: learning) recommender systems
(2: collecting) social psychology research
(3: using) transport information systems
what would we like to support?

28. sensed
context
reported
mood
“in the
moment”
infers
(responds)

29. what kind of problem is this?
device + application + user... combined
in the following: just the sensors

30. data collection for mobile apps
sense sleep
energy-accuracy trade-off

32. adaptive sensor sampling
interval (50%)
adaptive
-5 % battery
- 40% accuracy
co-location
-8 % battery
- 34% accuracy
Rachuri et al. (2011)
accelerometer
sensing library now available: emotionsense.org

33. ongoing work...

35. (1: learning) recommender systems
(2: collecting) social psychology research
(3: using) transport information systems
what would we like to support?

36. (3) transport
crowd-sourcing mobility & status data
follows previous work analysing and comparing
transport behavioural and off
i cial data
Lathia et al. (2012)

41. some open questions:

42. (1) privacy vs. effectiveness
the “security” of linking data

43. (2) do we need an “all encompassing” app?
what does it assume about using a phone?

44. (3) how do we go beyond measurement?

45. (4) software vs.
hardware race

46. (5) what happens if we succeed?
depressed
inactive
?

47. G. Adomavicius, A. Tuzhilin. “Context-Aware Recommender Systems.” In Recommender Systems
Handbook.
M. Bazire, P. Brezillon. “Understanding Context Before Using it.” In 5th
International Conference on
Modeling and Using Context, 2005.
K. Church, B. Smyth. “Who, What, Where, & When: A New Approach to Mobile Search.” In IUI 2008.
N. Lathia et al. “Individuals Among Commuters: Building Personalised Transport Information
Services from Fare Collection Systems” In Pervasive and Mobile Computing, 2012.
A. Noulas, S. Scellato, N. Lathia, C. Mascolo. “Mining User Mobility Features for Next Place
Prediction in Location-based Services.” In IEEE International Conference on Data Mining 2012a.
A. Noulas, S. Scellato, N. Lathia, C. Mascolo. “A Random Walk Around the City: New Venue
Recommendation in Location-Based Social Networks.” In International Conference on Social
Computing 2012b.
G. Miller. “The Smartphone Psychology Manifesto.” In Perspectives on Psychological Science 7(3).
2012.
D. Quercia, N. Lathia, F. Calabrese, G. Di Lorenzo, J. Crowcroft. “Recommending Social Events from
Mobile Phone Location Data.” In IEEE ICDM 2010, Sydney, Australia.
K. Rachuri et al. “SociableSense: Exploring the Trade-Offs of Adaptive Sampling and Computation
Off
l oading for Social Sensing.” In MobiCom 2011.
K. Rachuri et al. “METIS: Exploring Mobile Phone Sensing Off
l oading for Eff
i ciently Supporting
Social Sensing Applications.” To appear, 2013.
Applications: http://www.emotionsense.org, http://www.tubestar.co.uk
References:

48. contact:
neal.lathia@cl.cam.ac.uk
@neal_lathia