Learning to Rank for Personalized News Article Retrieval

Lorand Dali, Blaž Fortuna, Jan Rupnik
Proceedings of the First Workshop on Applications of Pattern Analysis, PMLR 11:152-159, 2010.

Abstract

This paper aims to tackle the very interesting and important problem of user personalized ranking of search results. The focus is on news retrieval and the data from which the ranking model is learned was provided by a large online newspaper. The personalized news search ranking model which we have developed takes into account not only document content and metadata, but also data specific to the user such as age, gender, job, income, city, country etc. All the user specific data is provided by the user himself when registering to the news site.

Cite this Paper

BibTeX
@InProceedings{pmlr-v11-dali10a, title = {Learning to Rank for Personalized News Article Retrieval}, author = {Dali, Lorand and Fortuna, Blaž and Rupnik, Jan}, booktitle = {Proceedings of the First Workshop on Applications of Pattern Analysis}, pages = {152--159}, year = {2010}, editor = {Diethe, Tom and Cristianini, Nello and Shawe-Taylor, John}, volume = {11}, series = {Proceedings of Machine Learning Research}, address = {Cumberland Lodge, Windsor, UK}, month = {01--03 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v11/dali10a/dali10a.pdf}, url = {https://proceedings.mlr.press/v11/dali10a.html}, abstract = {This paper aims to tackle the very interesting and important problem of user personalized ranking of search results. The focus is on news retrieval and the data from which the ranking model is learned was provided by a large online newspaper. The personalized news search ranking model which we have developed takes into account not only document content and metadata, but also data specific to the user such as age, gender, job, income, city, country etc. All the user specific data is provided by the user himself when registering to the news site.} }
Endnote
%0 Conference Paper %T Learning to Rank for Personalized News Article Retrieval %A Lorand Dali %A Blaž Fortuna %A Jan Rupnik %B Proceedings of the First Workshop on Applications of Pattern Analysis %C Proceedings of Machine Learning Research %D 2010 %E Tom Diethe %E Nello Cristianini %E John Shawe-Taylor %F pmlr-v11-dali10a %I PMLR %P 152--159 %U https://proceedings.mlr.press/v11/dali10a.html %V 11 %X This paper aims to tackle the very interesting and important problem of user personalized ranking of search results. The focus is on news retrieval and the data from which the ranking model is learned was provided by a large online newspaper. The personalized news search ranking model which we have developed takes into account not only document content and metadata, but also data specific to the user such as age, gender, job, income, city, country etc. All the user specific data is provided by the user himself when registering to the news site.
RIS
TY - CPAPER TI - Learning to Rank for Personalized News Article Retrieval AU - Lorand Dali AU - Blaž Fortuna AU - Jan Rupnik BT - Proceedings of the First Workshop on Applications of Pattern Analysis DA - 2010/09/30 ED - Tom Diethe ED - Nello Cristianini ED - John Shawe-Taylor ID - pmlr-v11-dali10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 11 SP - 152 EP - 159 L1 - http://proceedings.mlr.press/v11/dali10a/dali10a.pdf UR - https://proceedings.mlr.press/v11/dali10a.html AB - This paper aims to tackle the very interesting and important problem of user personalized ranking of search results. The focus is on news retrieval and the data from which the ranking model is learned was provided by a large online newspaper. The personalized news search ranking model which we have developed takes into account not only document content and metadata, but also data specific to the user such as age, gender, job, income, city, country etc. All the user specific data is provided by the user himself when registering to the news site. ER -
APA
Dali, L., Fortuna, B. & Rupnik, J.. (2010). Learning to Rank for Personalized News Article Retrieval. Proceedings of the First Workshop on Applications of Pattern Analysis, in Proceedings of Machine Learning Research 11:152-159 Available from https://proceedings.mlr.press/v11/dali10a.html.

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