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We present a recommendation algorithm for online news based on collective intelligence and content. When a user asks for personalized news, our algorithm recommends news articles that (i) are popular among the members of the online community (the collective intelligence part), and (ii) are similar in content to the news articles the user has read in the past (the content part). Our algorithm computes its recomendations based on the collective behavior of the online users, as well as on the feedback the users provide to the algorithm’s recommendations. The users’ feedback can moreover be used to measure the effectiveness of our recomendation algorithm in terms of the information retrieval concepts of precision and recall. The cornerstone of our recommendation algorithm is a basic relevance algorithm that computes how relevant a news article is to a given user. This basic relevance algorithm can be optimized in order to obtain a faster online response at the cost of minimal offline computations. Moreover, it can be turned into an approximated algorithm for an even faster online response.

A RECOMMENDATION ALGORITHM FOR PERSONALIZED ONLINE NEWS BASED ON COLLECTIVE INTELLIGENCE AND CONTENT