This work describes a novel strategy implementing a context-aware recommendation system. It has been conceived to offer an intelligent selection of micro-services used to orchestrate networks of smart objects taking into account users’ needs and preferences. The recommendation offering dynamically evolves depending on users’ micro-service management patterns and users’ context. The complete system has been designed within Dempster-Shafer evidential theory framework, ensuring uncertainty support both at context acquisition and at recommendation configuration level.

1. An evidential and context-aware recommendation
strategy to enhance interactions with smart spaces
Josué Iglesias, Ana M. Bernardos, José R. Casar
josue@grpss.ssr.upm.es
HAIS 2013
Grupo de Procesado de Datos y Simulación
ETSI de Telecomunicación
Universidad Politécnica de Madrid

10. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
 application domain:
smart space personalization
 recommendation strategy
 (evidential theory)
 contextual resource prioritization
 contextual recommendation update
 test and evaluation
 discussion and future works
contents

11. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
contents
 application domain:
smart space personalization
 recommendation strategy
 (evidential theory)
 contextual resource prioritization
 contextual recommendation update
 test and evaluation
 discussion and future works

12. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
application domain: smart space personalization

13. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
application domain: smart space personalization
behaviours
smart space

14. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
a) smart object
manager installation
 module publishing
 proximity detection
event
condition(s)
action(s)
c) behaviours evaluation
(and action execution)
event
generation
action
execution
b) behaviours configuration
 ECA rules
 e.g., “IF I’m approaching home
AND no one is there THEN turn
the heater on”
more details in
1. Bernardos, A.M., Casar, J.R., Cano, J., Bergesio, L.: Enhancing interaction with
smart objects through mobile devices. In: Proceedings of the 9th ACM
international symposium on Mobility management and wireless access. MobiWac
’11, New York, NY, USA, ACM (2011) 199–202
application domain: smart space personalization
shared behaviours
market

15. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
a) smart object
manager installation
 module publishing
 proximity detection
event
condition(s)
action(s)
c) behaviours evaluation
(and action execution)
event
generation
action
execution
b) behaviours configuration
 ECA rules
 e.g., “IF I’m approaching home
AND no one is there THEN turn
the heater on”
more details in
1. Bernardos, A.M., Casar, J.R., Cano, J., Bergesio, L.: Enhancing interaction with
smart objects through mobile devices. In: Proceedings of the 9th ACM
international symposium on Mobility management and wireless access. MobiWac
’11, New York, NY, USA, ACM (2011) 199–202
application domain: smart space personalization
shared behaviours
market
proposal:
recommendation strategy
(general approach)

16. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
contents
 application domain:
smart space personalization
 recommendation strategy
 (evidential theory)
 contextual resource prioritization
 contextual recommendation update
 test and evaluation
 discussion and future works


17. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
objects
services
people
sensors
resources
contextstates
contextual
pre-filtering
contextual
resource
prioritization
resources
resources’
recommendation strategy
user’s
profile
contextual
recommendation
update
context-aware
recommendation
system

18. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
objects
services
people
sensors
user’s
profile
resources
contextstates
context-aware
recommendation
system
contextual
pre-filtering
contextual
resource
prioritization
resources
resources’
recommendation strategy
contextual
recommendation
update
recommendation

19. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
objects
services
people
sensors
resource management patterns
contextual
resource
prioritization
resources
resources’
recommendation strategy
user’s
profile
context-aware
recommendation
system
contextstates
resources
contextual
pre-filtering
recommendation
contextual
recommendation
update

20. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
objects
services
people
sensors
contextual
pre-filtering
resources
resources’
recommendation strategy
user’s
profile
context-aware
recommendation
system
resource management patterns
recommendation
contextual
recommendation
update
contextual
resource
prioritization
contextstates
resources

21. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
contents
 application domain:
smart space personalization
 recommendation strategy
 (evidential theory)
 contextual resource prioritization
 contextual recommendation update
 test and evaluation
 discussion and future works


22. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
mathematical method for handling subjective beliefs (evidences)
frame of discernment
(set of hypotheses)
belief mass function
1 2
, , , N
h h h
: 2 [0,1]m
| 2
( ) 1
i i
i
A A
m A
( ) 0.1
( ) 0.5
( ) 0.2
( ) ( ) 0.,
) 0
2
,
,
(
abc
a c
m
m
m
a
m
a b cm m
b
, ,
2 , ,, , , , , , , , , ,abc
abc
a b a b a c b a b c
b
c
a c
c
e.g.,
e.g.,
abc
ab ac bc
a b c
precise
vague
dempster-shafer evidential theory basis
recommendation strategy

23. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
mathematical method for handling subjective beliefs (evidences)
frame of discernment
(set of hypotheses)
belief mass function
|
( ) ( )
i i
i
B A B
Pls A m B
|
( ) ( )
i i
i
B B A
Bel A m B
1 2
, , , N
h h h
: 2 [0,1]m
| 2
( ) 1
i i
i
A A
m A
( ) 0.1
( ) 0.5
( ) 0.2
( ) ( ) 0.,
) 0
2
,
,
(
abc
a c
m
m
m
a
m
a b cm m
b
belief
plausibility
(·), (·)Bel Pls
, ,
2 , ,, , , , , , , , , ,abc
abc
a b a b a c b a b c
b
c
a c
c
belief interval
e.g.,
e.g.,
abc
ab ac bc
a b c
precise
vague
dempster-shafer evidential theory basis
recommendation strategy

24. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
mathematical method for handling subjective beliefs (evidences)
frame of discernment
(set of hypotheses)
belief mass function
|
( ) ( )
i i
i
B A B
Pls A m B
|
( ) ( )
i i
i
B B A
Bel A m B
1 2
, , , N
h h h
: 2 [0,1]m
| 2
( ) 1
i i
i
A A
m A
( ) 0.1
( ) 0.5
( ) 0.2
( ) ( ) 0.,
) 0
2
,
,
(
abc
a c
m
m
m
a
m
a b cm m
b
belief
plausibility
(·), (·)Bel Pls
Dempster’s rule of
combination
, ,
2 , ,, , , , , , , , , ,abc
abc
a b a b a c b a b c
b
c
a c
c
1 2
, |
1 2
1 2
, |
( )· ( )
( )( )
1 ( )· ( )
B C B C A
B C B C
m B m C
m m A
m B m C
belief interval
e.g.,
e.g.,
abc
ab ac bc
a b c
precise
vague
dempster-shafer evidential theory basis
recommendation strategy

25. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
contents
 application domain:
smart space personalization
 recommendation strategy
 (evidential theory)
 contextual resource prioritization
 contextual recommendation update
 test and evaluation
 discussion and future works



26. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
resource prioritization
recommendation strategy
objects
services
people
sensors
contextual
pre-filtering
resources
resources’
user’s
profile
context-aware
recommendation
system
resource management patterns
recommendation
contextual
recommendation
update
contextual
resource
prioritization
contextstates
resources

27. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
resource prioritization
recommendation strategy
context-resource
evidential mapping
and propagation
evidential fusion
evidential
prioritization
strategy

28. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
resource prioritization
recommendation strategy
context-resource
evidential mapping
and propagation
evidential fusion
evidential
prioritization
strategy

29. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
resource prioritization
recommendation strategy
context-resource
evidential mapping
and propagation
evidential fusion
evidential
prioritization
strategy
current location -
historic location -
time -
gender -
age -
etc. -

30. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
resource prioritization
recommendation strategy
1 2
, |
1 2
1 2
, |
( )· ( )
( )( )
1 ( )· ( )
B C B C A
B C B C
m B m C
m m A
m B m C
Dempster’s rule of
combination
context-resource
evidential mapping
and propagation
evidential fusion
evidential
prioritization
strategy

31. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
resource prioritization
recommendation strategy
context-resource
evidential mapping
and propagation
evidential fusion
evidential
prioritization
strategy

32. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
resource prioritization
recommendation strategy
context-resource
evidential mapping
and propagation
evidential fusion
evidential
prioritization
strategy

33. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
resource prioritization
recommendation strategy
context-resource
evidential mapping
and propagation
evidential fusion
evidential
prioritization
strategy
Minimax Regret Approach (MRA)

34. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
contents
 application domain:
smart space personalization
 recommendation strategy
 (evidential theory)
 contextual resource prioritization
 contextual recommendation update
 test and evaluation
 discussion and future works




35. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
objects
services
people
sensors
contextual
pre-filtering
resources
resources’
user’s
profile
context-aware
recommendation
system
resource management patterns
recommendation
contextual
resource
prioritization
contextstates
resources
contextual
recommendation
update
recommendation update
recommendation strategy

36. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
recommendation update
recommendation strategy
context
shared behaviours
market
management operations
create
execute
share
delete
(de)activate
download
modify
contextual
management of
resources
quantification
recommendation
update

37. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
recommendation update
recommendation strategy
context
shared behaviours
market
management operations
create
execute
share
delete
(de)activate
download
modify
context variable
current location
historic location
time
gender
age
etc.
context state resource id
contextual
management of
resources
quantification
recommendation
update

38. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
recommendation update
recommendation strategy
context
shared behaviours
market
management operations
create
execute
share
delete
(de)activate
download
modify
context variable
current location
historic location
time
gender
age
etc.
context state resource id
contextual
management of
resources
quantification
recommendation
update
distribute resource consumption
over context
multi-user average

39. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
recommendation update
recommendation strategy
context
shared behaviours
market
management operations
create
execute
share
delete
(de)activate
download
modify
contextual
management of
resources
quantification
recommendation
update
multi-user average
events
+0.02 
+0.11 
+0.05 
−0.04 
−0.01 
+0.12 
+0.07 

40. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
recommendation update
recommendation strategy
context
shared behaviours
market
management operations
create
execute
share
delete
(de)activate
download
modify
contextual
management of
resources
quantification
recommendation
update
multi-user average
+0.02 
+0.11 
+0.05 
−0.04 
−0.01 
+0.12 
+0.07 
events

41. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
contents
 application domain:
smart space personalization
 recommendation strategy
 (evidential theory)
 contextual resource prioritization
 contextual recommendation update
 test and evaluation
 discussion and future works






42. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
test and evaluation
users
management operations
download +0.01 
delete −0.01 
resources
context variables
location (7 symbolic locations)
time (4 temporal parts of day)
10
2
3
2
recommendation update simulation

43. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
test and evaluation
users
management operations
download +0.01 
delete −0.01 
resources
context variables
location (7 symbolic locations)
time (4 temporal parts of day)
10
2
3
2
recommendation update simulation

44. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
test and evaluation
users
management operations
download +0.01 
delete −0.01 
resources
context variables
location (7 symbolic locations)
time (4 temporal parts of day)
10
2
3
2
recommendation update simulation

45. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
test and evaluation
users
management operations
download +0.01 
delete −0.01 
resources
context variables
location (7 symbolic locations)
time (4 temporal parts of day)
10
2
3
2
recommendation update simulation

46. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
test and evaluation
users
management operations
download +0.01 
delete −0.01 
resources
context variables
location (7 symbolic locations)
time (4 temporal parts of day)
10
2
3
2
recommendation update simulation

47. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
contents
 application domain:
smart space personalization
 recommendation strategy
 (evidential theory)
 contextual resource prioritization
 contextual recommendation update
 test and evaluation
 discussion and future works







48. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
discussion and future works
• context-aware recommendations of micro-services (behaviours)
for smart space personalization
• dempster-shafer evidential theory applied to:
– sensor modelling
– micro-services priority calculation
– priority update

49. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
discussion and future works
• context-aware recommendations of micro-services (behaviours)
for smart space personalization
• dempster-shafer evidential theory applied to:
– sensor modelling
– micro-services priority calculation
– priority update

50. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
discussion and future works
• context-aware recommendations of micro-services (behaviours)
for smart space personalization
• dempster-shafer evidential theory applied to:
– sensor modelling
– micro-services priority calculation
– priority update

51. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
discussion and future works
• context-aware recommendations of micro-services (behaviours)
for smart space personalization
• dempster-shafer evidential theory applied to:
– sensor modelling
– micro-services priority calculation
– priority update

52. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
any question?

53. josue@grpss.ssr.upm.esInternational Conference on Hybrid Artificial Intelligence Systems – HAIS 2013
recommendation update algorithm
just singletons update
if Σsingletons > 1
• normalize singletons
• remaining (non singletons) = 0
• update jump factor
if Σsingletons <= 1
• if Σall < 1  remaining to total ignorance
• if Σall > 1  proportionally decrease nonSingls.