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.

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).

6. Outline of the talk
 Introduction: motivation of the problem
 User profile management
 Automatic learning of user interests
 Evaluation
 Conclusions

7. 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

8. 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.

9. 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

10. Outline of the talk
 Introduction: motivation of the problem
 User profile management
 Automatic learning of user interests
 Evaluation
 Conclusions

11. Selected / Over-ranked alternatives
Over-ranked alternatives

12. Increase preference value

13. Smaller increase of preference value

14. Decrease preference value

15. 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.

16. 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.

17. Outline of the talk
 Introduction: motivation of the problem
 User profile management
 Automatic learning of user interests
 Evaluation
 Conclusions

18. 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

21. Outline of the talk
 Introduction: motivation of the problem
 User profile management
 Automatic learning of user interests
 Evaluation
 Conclusions

22. 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.

23. 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