Abstract
A recommendation system (or recommender) is an algorithm whose goal is to recommend products to potential users. To achieve its task, it uses information about some user preferences.
We present recommenders that use information about the preferences of only a very small subset of users (called a committee) on a very small set of products called the witness products set. The main interest of our approach compared to previous ones is that it needs substantially less data for ensuring a very good quality of recommendation.
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Hémon, S., Largillier, T., Peyronnet, S. (2010). Partial Ranking of Products for Recommendation Systems. In: Buccafurri, F., Semeraro, G. (eds) E-Commerce and Web Technologies. EC-Web 2010. Lecture Notes in Business Information Processing, vol 61. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15208-5_24
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DOI: https://doi.org/10.1007/978-3-642-15208-5_24
Publisher Name: Springer, Berlin, Heidelberg
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