A General Architecture for an Emotion-aware Content-based Recommender System
Fedelucio Narducci, Marco De Gemmis, Pasquale Lops
3rd Empire Workshop
RecSys 2015, Vienna, Austria, 16-20 September 2015
Driving Behavioral Change for Information Management through Data-Driven Gree...
A General Architecture for an Emotion-aware Content-based Recommender System
1. A General Architecture for an Emotion-aware
Content-based Recommender System
Fedelucio Narducci
Dept. of Computer Science
University of Bari ‘Aldo Moro’
Italy
Marco De Gemmis
Dept. of Computer Science
University of Bari ‘Aldo Moro’
Italy
Pasquale Lops
Dept. of Computer Science
University of Bari ‘Aldo Moro’
Italy
name.surname@uniba.it
Vienna, Austria, 19th September 2015
2. outline
• background and motivations
• general architecture for an emotion-aware
content-based recommender system
• emotion analysis services
• experimental evaluation
• conclusions and future work
3. emotions & decision making
• emotions influence the decision making process
during which, brain areas related to emotions are stimulated1
in the next few years…
I will have a stable economic position,
I am getting married,
I can buy a house
in the next months…
my postdoc will be ended,
I will be out of work, I will beg,
I can’t buy a house
1G. L. Clore, N. Schwarz, and M. Conway, “Affective causes and consequences of social information processing”, Handbook of social cognition, vol. 1, pp. 323-417, 1994.
A. Bechara, “Risky business: emotion, decision-making, and addiction," Journal of Gambling Studies, vol. 19, no. 1, pp. 23-51, 2003.
4. emotions &
recommendations
• “emotions are crucial for
user’s decision making in
recommendation process”1
• thanks to social networks, users
disseminate data related to their
emotions on the Web
• on April 2013, Facebook allows
users to choose an emoticon to
express their mood
1 G. Gonzalez, J. L. De La Rosa, M. Montaner, and S. Delfin, “Embedding emotional context in recommender systems”, in Data Engineering
Workshop, 2007 IEEE 23rd International Conference on Data Engineering, pp. 845-852.
5. emotional models
• discrete
basic emotions
identified by labels
• dimensional
emotion is a point in a
multidimensional space
• componential
emotions elicited by a
cognitive evaluation of
antecedent situations
6. a general architecture for a
EA Content-based RS
Content
Analyzer
Profile
Learner
Recommender
Emotion
Analyzer
Item
descriptions
Processed
Items
Rated
items
Suggested
Items
7. a general architecture for an
EARS
Content
Analyzer
Profile
Learner
Recommender
Emotion
Analyzer
Item
descriptions
Processed
Items
Rated
items
Suggested
Items
Analyzes unstructured
text and performs NLP
tasks on item descriptions
and text associated to
user emotional state
8. a general architecture for an
EARS
Content
Analyzer
Profile
Learner
Recommender
Emotion
Analyzer
Item
descriptions
Processed
Items
Rated
items
Suggested
Items
Generates a user profile.
The user profile has two
dimensions: preferences,
emotion
9. a general architecture for an
EARS
Content
Analyzer
Profile
Learner
Recommender
Emotion
Analyzer
Item
descriptions
Processed
Items
Rated
items
Suggested
Items
Matches user profile and
item representations. Both
user profile and items are
p r o v i d e d w i t h a n
emotional label
10. a general architecture for an
EARS
Content
Analyzer
Profile
Learner
Recommender
Emotion
Analyzer
Item
descriptions
Processed
Items
Rated
items
Suggested
Items
Implements one or more
s e n t i m e n t - a n a l y s i s
algorithms able to assign
emotional labels to a NL
text
11. @work - emotion analysis
• text classifiers
three different classifiers are learned
on two distinct labelled datasets on
the Ekman emotional model
• thesauri
for each emotion of the Ekman model
a thesaurus is automatically generated
by exploiting the WordNet synsets
two approaches combined by Borda
count
synonym set
n timesseed
seed
12. experimental evaluation
• domain: music recommendation
• training datasets: LiveJournal1, Aman2
• music dataset: ~40,000 music tracks from
Last.fm
• 578 songs evaluated by 77 users
1https://snap.stanford.edu/data/soc-LiveJournal1.html
2S. Aman and S. Szpakowicz, Identifying expressions of emotion in text, in Text, Speech and Dialogue. Springer,
2007, pp. 196-205.
13. recommendation
approaches
• favorite
two songs were randomly chosen from the set of tracks of the
favorite artists (from Facebook), labeled with the user entry
emotion
• not favorite
two songs were randomly chosen from the set of tracks labeled with
the user entry emotion, but not belonging to favorite artists
• random (baseline)
two songs were randomly chosen by filtering out favorite
artists and tracks labeled with the user entry emotion
14. research questions
• RQ1: Is the defined algorithm able to effectively
extract an emotion from a NL text?
• RQ2: Is the emotion detection able to improve
the user rating?
• RQ3: Is our model able to effectively associate
an emotion to an item provided with an
unstructured text?
15. user study
• Users were asked to
• express her emotional state by a sentence and
validate the emotional label automatically
assigned by the system
• allow the extraction of her musical
preferences from Facebook
• receive suggestions according to her
emotional state or can choose a different one
• evaluate a set of recommendations by answering
to two questions
16. results - emotion analysis
Emotion # Precision Recall F1
ANGER 8 0.25 0.50 0.33
DISGUST 2 1.00 0.50 0.67
FEAR 7 0.43 0.43 0.43
JOY 35 0.84 0.74 0.79
SADNESS 23 0.67 0.61 0.64
SURPRISE 2 1.00 0.50 0.67
0
0,25
0,5
0,75
1
ANGER (8) DISGUST (2) FEAR (7) JOY (35) SADNESS (23)SURPRISE (2)
Precision Recall F1
17. results
Do you like this song?
0
0,25
0,5
0,75
1
Favorite Not Favorite Random
YES/PART. NO
Is this song suitable with your
emotion?
0
0,225
0,45
0,675
0,9
Favorite Not Favorite Random
YES/PART. NO
18. conclusions & future work
• Contributions
• designing and testing a general architecture for an
emotion-aware content based recsys
• implementing sentiment analysis services freely
available online1
• implementing a prototypal music recommender
system that exploits the proposed architecture and
services2
• Future Work
• testing new sentiment analysis, recommendation
algorithms, emotion models also in other domains
1http://193.204.187.192:8080/MyEmotionsRest/webresources/service/getEmotion/<text>
2http://193.204.187.192:8080/eMusic/