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Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
Dynamic learning of keyword-based preferences for news recommendation (WI-2014)
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Dynamic learning of keyword-based preferences for news recommendation (WI-2014)

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Presentation at workshop on recommender systems at WI-2014. …

Presentation at workshop on recommender systems at WI-2014.
Automatic learning of keyword-based preferences through the analysis of the implicit information provided by the interaction of the user.

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  • 1. Dynamic learning of keyword-based preferences for news recommendation A.Moreno, L.Marin, D.Isern, D.Perelló ITAKA-Intelligent Tech. for Advanced Knowledge Acquisition Departament d’Enginyeria Informàtica i Matemàtiques Universitat Rovira i Virgili, Tarragona http://deim.urv.cat/~itaka
  • 2. Outline of the talk  Introduction: motivation of the problem  User profile management  Automatic learning of user interests  Evaluation  Conclusions
  • 3. Outline of the talk  Introduction: motivation of the problem  User profile management  Automatic learning of user interests  Evaluation  Conclusions
  • 4. Introduction: preference learning  Important issue in recommender systems: discover the user interests to provide accurate recommendations.  User preferences may be explicitly given by the user or may be inferred through the analysis of his/her actions.  We focus our attention on the case in which the objects to be recommended are purely textual (e.g. News).
  • 5. Outline of the talk  Introduction: motivation of the problem  User profile management  Automatic learning of user interests  Evaluation  Conclusions
  • 6. Representation of preferences  The user profile will store a dynamic set of keywords. Each of them will have a positive/negative level of preference, in the range [-100, 100] Manchester United +80 Angela Merkel -90 tennis 0
  • 7. Representation of a textual object  Given a corpus of textual documents, an object (news) will be represented by a set of n relevant keywords, determined by the standard TF-IDF measure.
  • 8. Evaluation of a textual object  Given a user profile P and a document d, the score assigned to the document in the first ranking phase is the addition of the user preferences on the document’s keywords Keywords of the document Preference value of keyword w
  • 9. Outline of the talk  Introduction: motivation of the problem  User profile management  Automatic learning of user interests  Evaluation  Conclusions
  • 10. Selected / Over-ranked alternatives Over-ranked alternatives
  • 11. Increase preference value
  • 12. Smaller increase of preference value
  • 13. Decrease preference value
  • 14. Summary of learning algorithm (I)  Increase the preference value of the keywords of the selected news that do not appear in the over-ranked alternatives.  The more over-ranked alternatives, the greater the increase  Increase (in a smaller degree) the preference value of the keywords of the selected news that appear in the over-ranked alternatives.  The more repetitions on the over-ranked alternatives, the smaller the increase.
  • 15. Summary of learning algorithm (II)  Decrease the preference value of the keywords of the over-ranked alternatives that do not appear in the selected news.  The more repetitions on the over-ranked alternatives, the greater the decrease. The amounts of increase/decrease were determined empirically, and the details may be found in the paper.
  • 16. Outline of the talk  Introduction: motivation of the problem  User profile management  Automatic learning of user interests  Evaluation  Conclusions
  • 17. Evaluation framework  Retrieval of 6000 news from The Guardian.  Definition of an ideal profile to be learnt.  Random generation of 10 initial profiles.  A single test consists in a series of 400 recommendations over 6000 alternatives, considering 15 alternatives at each step and 30 keywords/news  After each recommendation, the normalised distance between the current profile P and the ideal one I is calculated
  • 18. Outline of the talk  Introduction: motivation of the problem  User profile management  Automatic learning of user interests  Evaluation  Conclusions
  • 19. Conclusions  User preferences on textual documents may be efficiently learned in an implicit way if the user has a frequent interaction with the system.  In the future work we intend to introduce semantic information in the learning process  If a user likes tennis/football/golf, the system could infer a general interest on sports.  Treat natural language phenomena like synonymity and polysemy.
  • 20. Dynamic learning of keyword-based preferences for news recommendation A.Moreno, L.Marin, D.Isern, D.Perelló ITAKA-Intelligent Tech. for Advanced Knowledge Acquisition Departament d’Enginyeria Informàtica i Matemàtiques Universitat Rovira i Virgili, Tarragona http://deim.urv.cat/~itaka

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