Learning to Rank for Personalized News Article Retrieval
Lorand Dali, Blaž Fortuna, Jan Rupnik · 2010
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 specic to the user such as age, gender, job, income, city, country etc. All the user specic data is provided by the user himself when registering to the news site.