(Apologies for multiple postings)
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CLEF-NEWSREEL 2014: Call for Participation
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http://clef-newsreel.org
News article recommendation differs from the most “traditional” recommender scenarios which have been studied in literature. Instead of having a static set of users and items, news portals deal with a constantly changing set of news articles. Besides, hardly anything is known about the users and their interests. In addition, the short lifecycle of items and the strict time-constraints for recommending news articles put specific requirements on the implementation of successful recommendation strategies.
In CLEF-NEWSREEL, researchers can benchmark news recommendation techniques in real-time by recommending news articles to actual users that visit commercial news portals to satisfy their individual information needs. In addition, they can predict users’ clicks on news article in an offline dataset. The lab is split into two tasks:
Task 1: Interaction Prediction based on Historical Data - Predict users’ clicks on news articles in a large dataset consisting of interactions between users and a variety of news portals.
Task 2: Recommending News Articles in Real-Time - Provide news article recommendations in a living lab scenario for millions of users who browse various news portals. Participants will be given access to the requests of millions of users who read news articles online. They have to provide recommendations (“articles that the readers might be interested in”) under real conditions such as time constrains and limited knowledge about the users’ interests. Participants can constantly observe the performance of their algorithms and fine-tune their approaches for a longer period of time. The best performing algorithms will be determined during pre-defined time periods.
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Important Dates
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now open Registration
Mar 1, 2014 Submission Track 1 results
Jun 16, 2014 Submission Working notes
Sep 15-18, 2014 CLEF 2014
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