Recommender systems based on methods such as collaborative and content-based filtering rely on extensive user profiles and item descriptors as well as on an extensive history of user preferences. Such methods face a number of challenges; including the cold-start problem in systems characterized by irregular usage, privacy concerns, and contexts where the range of indicators representing user interests is limited. We describe a recommender algorithm that builds a model of collective preferences independently of personal user interests and does not require a complex system of ratings. The performance of the algorithm is analyzed on a large transactional data set generated by a real-world dietary intake recall system.
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