Learning to Rank for Personalized News Article Retrieval

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.

• Type: Journal
• Year: 2010
• Where: JMLR
• Authors: Lorand Dali, Blaz Fortuna, Jan Rupnik
• Keywords: JMLR 2010, Ranking, User, User Specific Data