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

Outline of the talk

Introduction: motivation of the problem

User profile management

Automatic learning of user interests

Evaluation

Conclusions

Outline of the talk

Introduction: motivation of the problem

User profile management

Automatic learning of user interests

Evaluation

Conclusions

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).

Outline of the talk

Introduction: motivation of the problem

User profile management

Automatic learning of user interests

Evaluation

Conclusions

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

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.

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

Outline of the talk

Introduction: motivation of the problem

User profile management

Automatic learning of user interests

Evaluation

Conclusions

Selected / Over-ranked alternatives

Over-ranked alternatives

Increase preference value

Smaller increase of preference value

Decrease preference value

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.

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.

Outline of the talk

Introduction: motivation of the problem

User profile management

Automatic learning of user interests

Evaluation

Conclusions

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

Outline of the talk

Introduction: motivation of the problem

User profile management

Automatic learning of user interests

Evaluation

Conclusions

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.

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