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Speaker:
Semantic Web Access and PersonalizationSemantic Web Access and Personalization
UNIVERSITY OF BARI
ALDO MORO
DEPAR...
Decisions and emotions
23/09/2415 2/24
A person facing a choosing problem has to consider different solutions and
take a d...
Decisions and emotions
23/09/2415 3/24
Users might evaluate the consequences of the possible options by taking into
accoun...
Decisions and emotions
23/09/2415 4/24
Recommender Systems supporting Decision Making through AnalysisRecommender Systems ...
Influence of emotions
• Emotions are considered as external forces influencing an
otherwise non-emotional process:
– Immed...
Recommender Systems
Recommender Systems (RSs) are tools which implements
information filtering strategies to deal with inf...
Emotions and Recommender Systems
Relevance of affect in RSs was discussed in literature, that showed
an increment of recom...
Study Purpose
Our work aims at defining a general framework in which emotions
play a relevant role because they are embedd...
Emotion Detection: hard task
23/09/2415 9/24
Recommender Systems supporting Decision Making through AnalysisRecommender Sy...
Emotion Detection
Emotions during the decision process can be detected using implicit or
explicit strategies.
23/09/2415 1...
Emotion Detection
23/09/2415 11/24
Recommender Systems supporting Decision Making through AnalysisRecommender Systems supp...
Emotion Detection and Text
23/09/2415 12/24
Recommender Systems supporting Decision Making through AnalysisRecommender Sys...
Affective profile
23/09/2415 13/24
Recommender Systems supporting Decision Making through AnalysisRecommender Systems supp...
Personality traits
23/09/2415 14/24
Recommender Systems supporting Decision Making through AnalysisRecommender Systems sup...
Historical Cases
23/09/2415 15/24
Recommender Systems supporting Decision Making through AnalysisRecommender Systems suppo...
Historical Cases
23/09/2415 16/24
Recommender Systems supporting Decision Making through AnalysisRecommender Systems suppo...
Context and expertise
23/09/2415 17/24
Recommender Systems supporting Decision Making through AnalysisRecommender Systems ...
Emotional-Recommender System
23/09/2415 18/24
Recommender Systems supporting Decision Making through AnalysisRecommender S...
Emotional-Recommender System
23/09/2415 19/24
Recommender Systems supporting Decision Making through AnalysisRecommender S...
Emotional-Recommender System Pipeline
23/09/2415 20/24
Recommender Systems supporting Decision Making through AnalysisReco...
Emotional-Recommender System Pipeline
23/09/2415 21/24
Recommender Systems supporting Decision Making through AnalysisReco...
Adapting candidate solutions
23/09/2415 22/24
Recommender Systems supporting Decision Making through AnalysisRecommender S...
Cold start
23/09/2415 23/24
Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting ...
Remarks and Ongoing Work
23/09/2415 24/24
Recommender Systems supporting Decision Making through AnalysisRecommender Syste...
Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Anal...
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Recommender Systems supporting Decision Making through Analysis of User Emotions and Personality

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Presentation for AI*IA 2015 conference in Doctoral Consortium

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Recommender Systems supporting Decision Making through Analysis of User Emotions and Personality

  1. 1. Speaker: Semantic Web Access and PersonalizationSemantic Web Access and Personalization UNIVERSITY OF BARI ALDO MORO DEPARTMENT OF COMPUTER SCIENCE Recommender Systems supporting Decision Making through Analysis of User Emotions and Personality PhD. Student Marco Polignano 23-25 September 2015
  2. 2. Decisions and emotions 23/09/2415 2/24 A person facing a choosing problem has to consider different solutions and take a decision. Traditional approaches of behavioural decision making, consider choosing as a rational cognitive process that estimates which of various alternative choices would yield the most positive consequences, which does not necessarily entail emotions. Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality
  3. 3. Decisions and emotions 23/09/2415 3/24 Users might evaluate the consequences of the possible options by taking into account both positive and negative emotions associated with them and then select those actions that maximize positive emotions and minimize negative emotions. Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality
  4. 4. Decisions and emotions 23/09/2415 4/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality
  5. 5. Influence of emotions • Emotions are considered as external forces influencing an otherwise non-emotional process: – Immediate emotions are consequences of events that has recently affected the user. They are not directly connected with the decision but they influence the final choice. – Expected emotions are affects that the user supposed to prove as a consequence of the decision. She will choose the option that will maximize the positive expectations. 23/09/2415 5/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality
  6. 6. Recommender Systems Recommender Systems (RSs) are tools which implements information filtering strategies to deal with information overload and to support users into choosing tasks, by taking into account their preferences and contexts of choosing. RSs can adopt different filtering algorithms based on: the item content (description), the user activity, the knowledge of context, but usually they do not consider emotions. 23/09/2415 6/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality
  7. 7. Emotions and Recommender Systems Relevance of affect in RSs was discussed in literature, that showed an increment of recommendation performance using emotions as a context in a context-aware RS or affective label associated to the recommender items. Emotional feedback play different roles related to the acquisition of user preferences: 1. As a source of affective metadata for item modelling and building a preference model; 2. As an implicit relevance feedback for assessing user satisfaction. In this work, we focus on the first issue: the idea is to acquire affective features that might be exploited for user modelling. 23/09/2415 7/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality
  8. 8. Study Purpose Our work aims at defining a general framework in which emotions play a relevant role because they are embedded in the reasoning process. •Which techniques are most suitable to identify users’ emotions from their behaviour? •How to define a computational model of personality and emotions? •How to include the emotional computational model in a recommendation process? 23/09/2415 8/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality
  9. 9. Emotion Detection: hard task 23/09/2415 9/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality
  10. 10. Emotion Detection Emotions during the decision process can be detected using implicit or explicit strategies. 23/09/2415 10/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality Questionnaires* can be used to ask to the user the emotions that she is feeling. Users will check, from a list of Six-Ekman emotions*, wich are the emotions that better describe their current feeling. Explicit Strategies
  11. 11. Emotion Detection 23/09/2415 11/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality Implicit Strategies Often people are not able to explicate correctly emotions, and explicit strategies could not be enough to correct identify immediate emotions. Poria* present a multi-modal framework that uses audio, video and text sources to identify user emotions and to map them into the Ekman’s six emotions. The results show that hight precision can be achieved in the emotion detection task by combining different signals. http://www.iis.fraunhofer.de/ *Soujanya Poria et al. ”Towards an intelligent framework for multimodal affective data analysis.” Neural Networks 63. Pages: 104-116, 201
  12. 12. Emotion Detection and Text 23/09/2415 12/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality Research on this topic showed that both user personality traits and user emotional state can be inferred by adopting Natural Language Processing (NLP) techniques. Machine learning techniques have been also used for this purpose: one of the most useful framework adopted is SNoW*, a general purpose multi-class classifier. Strategies based on emotion lexicon are also popular. They usually identify key terms in sentences and, then check the emotions associated to each word in an emotion-based lexicon. In our proposal, we will evaluate different strategies for acquiring both emotions and personality traits. *Cecilia Ovesdotter Alm et al.: ”Emotions from text: machine learning for text-based emotion prediction.” Association for Computational Linguistics, 2005
  13. 13. Affective profile 23/09/2415 13/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality Personality Context and experience Historical Cases with Emotions AP = { P T, H C, C E } The user affective profile is an extension of the standard user profile used by RSs. It will be used by the RS to adapt its computational process and to generate recommendations according to emotions
  14. 14. Personality traits 23/09/2415 14/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality A 44-Item Big Five Inventory questionnaire proposed by John and Srivastava (1999) could be used to get the user personality traits. Another techniques that can be adopted is the automatic extraction of personality traits from Social network as showed by Golbeck (2011)
  15. 15. Historical Cases 23/09/2415 15/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality An historical decision case describes accurately the decision making task and emotions felt by the users. Emotion-aware RSs have to identify immediate emotions and forecast expected emotions. • The decision task can be divided in three stages in according to *Tkalckic: early, consuming and exit During all the decision, strategies of emotion identification from video, audio source will be stored. The process will be supported from strategies of emotion extract from Social Network posts, while an additional emotional value could be gathered from user asking her the emotion felt at decision time. *Marko Tkalcic, et al.: ”Affective recommender systems: the role of emotions in recommender systems.” RecSys11 October 23-27, 2011
  16. 16. Historical Cases 23/09/2415 16/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality A case contains early stage emotions, consuming stage emotions, exit stage emotions, and a description of the task. The description of the task is defined by: •Context of decision •Problem and elements among which choosing, decision taken •Explicating feedbacks in a scale from 1 to 10 to describe the utility of suggestions (1 not useful, 10 extremely useful). The historical case could be enriched with more features, for example a description of interaction between user and system, but we decide to simplify the situation for realizing a preliminary working framework.
  17. 17. Context and expertise 23/09/2415 17/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality The context is characterized by explicit features that describe user preferences in a specific domain. The expertise of user in the specific domain is the number of decisions taken in this domain, starting from an initial value obtained from a user ability questionnaire. The available contexts of applications will be defined a priori in a list of chooses because we do not focus on the context detection strategies.
  18. 18. Emotional-Recommender System 23/09/2415 18/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality The idea is to collapse all the information acquired about the emotional state of the user and her personality into an affective profile which stores both rational and irrational data about user decisions. The affective profile is a knowledge base that allows the RS to reason about past user’s choices, emotions felt during the decision process, contexts in which decisions were taken. An Emotion-aware RS will exploit case base reasoning to solve new problems by adapting past solutions in similar context, and taking into account the emotional state of the user, as well as personality traits.
  19. 19. Emotional-Recommender System 23/09/2415 19/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality
  20. 20. Emotional-Recommender System Pipeline 23/09/2415 20/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality 1. The Recommender has to identify users similar to the active user (the one for which suggestion must be provided). Similarity measures, like cosine similarity, are used on vectors obtained combining personality features and preference features in the specific context. 2. This set of users, including the active user, is used to identify decisions taken in the past that match the problem, the active user emotional state, and must have positive exit stage emotions or positive user feedback.
  21. 21. Emotional-Recommender System Pipeline 23/09/2415 21/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality 3. From the historical cases detected, candidate solutions are extracted and adapted (eg. filtered or ranked, adapted for increase serendipity ) according to the context of the problem. The obtained recommendations are proposed to the active user. 4. The decision taken, the problem, the emotional state of the user, and a feedback of the utility of recommendations are stored in the Emotional Case-Base, and are reused for future recommendations.
  22. 22. Adapting candidate solutions 23/09/2415 22/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality Different strategies are available to adapt, rank or filter the solutions: 1.Rank the solutions in according to user preferences in the specific emotional state to increase the utility of suggestions 2.Using the expertise of the user in the specific domain is possible to rank recommendations to support users with low experience. Less riskily solutions be suggested first than others. 3.Rank the solutions in according to the influence of emotional features on the user based on the risk that the decision involves. For example Schlosser* describes the role of emotions in risky and uncertain domains, where emotions influence the ability to consider all the relevant aspect of the decision and the quantity and quality of information interpreted. *What a feeling: the role of immediate and anticipated emotions in risky decisions. Journal of Behavioral Decision Making 26.1. Pages: 13-30, 2013
  23. 23. Cold start 23/09/2415 23/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality If poor data are available for the proposed computation pipeline, an inference from personality traits in the specific domain could be used to choose possible candidate solutions. For example, for people with an high value of ”Openness”, uncommon solutions can be selected as a candidate recommendations. Where will I take inference rules? •Psychological literature •Recent studies in Recommender Systems and Emotions •…
  24. 24. Remarks and Ongoing Work 23/09/2415 24/24 Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality Systems that support the decision making task, currently take into account emotions in a limited way, while we have proposed a solution able to embed emotions and personality traits into the recommendation process. And now…
  25. 25. Recommender Systems supporting Decision Making through AnalysisRecommender Systems supporting Decision Making through Analysis of User Emotions and Personalityof User Emotions and Personality

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