[HAIS'13] An evidential and context-aware recommendation strategy to enhance interactions with smart spaces

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

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

contents

application domain: smart space personalization

behaviours
smart space

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
shared behaviours
market

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
shared behaviours
market
proposal:
recommendation strategy
(general approach)

contents


objects
services
people
sensors
resources
contextstates
contextual
pre-filtering
contextual
resource
prioritization
resources
resources’
user’s
profile
contextual
recommendation
update
context-aware
recommendation
system

objects
services
people
sensors
user’s
profile
resources
contextstates
context-aware
recommendation
system
contextual
pre-filtering
contextual
resource
prioritization
resources
resources’
contextual
recommendation
update
recommendation

objects
services
people
sensors
resource management patterns
contextual
resource
prioritization
resources
resources’
user’s
profile
context-aware
recommendation
system
contextstates
resources
contextual
pre-filtering
recommendation
contextual
recommendation
update

objects
services
people
sensors
contextual
pre-filtering
resources
resources’
user’s
profile
context-aware
recommendation
system
recommendation
contextual
recommendation
update
contextual
resource
prioritization
contextstates
resources

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

(set of hypotheses)
|
( ) ( )
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

(set of hypotheses)
|
( ) ( )
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

contents



resource prioritization
objects
services
people
sensors
contextual
pre-filtering
resources
resources’
user’s
profile
context-aware
recommendation
system
recommendation
contextual
recommendation
update
contextual
resource
prioritization
contextstates
resources

context-resource
evidential mapping
and propagation
evidential fusion
evidential
prioritization
strategy

context-resource
evidential mapping
and propagation
evidential fusion
evidential
prioritization
strategy
current location -
historic location -
time -
gender -
age -
etc. -

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

context-resource
evidential mapping
and propagation
evidential fusion
evidential
prioritization
strategy
Minimax Regret Approach (MRA)

contents




objects
services
people
sensors
contextual
pre-filtering
resources
resources’
user’s
profile
context-aware
recommendation
system
recommendation
contextual
resource
prioritization
contextstates
resources
contextual
recommendation
update
recommendation update

context
shared behaviours
market
management operations
create
execute
share
delete
(de)activate
download
modify
contextual
management of
resources
quantification
recommendation
update

context
shared behaviours
market
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

context
shared behaviours
market
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

context
shared behaviours
market
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 

context
shared behaviours
market
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

contents






test and evaluation
users
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

contents







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

any question?

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.

[HAIS'13] An evidential and context-aware recommendation strategy to enhance interactions with smart spaces

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