A General Architecture for an Emotion-aware Content-based Recommender System
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The document presents a general architecture for an emotion-aware content-based recommender system, outlining how emotions influence decision-making and the importance of analyzing user emotions for effective recommendations. It describes various components of the system, including emotion analysis services, experimental evaluations, and user studies within a music recommendation context. Future work includes testing new algorithms and emotion models across different domains.
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/
- 19. thanks Fedelucio Narducci Dept. of Computer Science University of Bari ‘Aldo Moro’ Italy name.surname@uniba.it