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A recommender system (RecSys), or a recommendation system (sometimes replacing system with terms such as platform, engine, or algorithm), is a subclass of information filtering system that provides suggestions for items that are most pertinent to a particular user.
Matrix factorization is a class of collaborative filtering algorithms used in recommender systems.Matrix factorization algorithms work by decomposing the user-item interaction matrix into the product of two lower dimensionality rectangular matrices. [1]
Recommender Systems in industrial contexts – PHD thesis (2012) including a comprehensive overview of many collaborative recommender systems; Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. Adomavicius, G. and Tuzhilin, A. IEEE Transactions on Knowledge and Data Engineering 06.2005
As in user-user systems, similarity functions can use normalized ratings (correcting, for instance, for each user's average rating). Second, the system executes a recommendation stage. It uses the most similar items to a user's already-rated items to generate a list of recommendations.
Amylyx's ALS drug failed to get a recommendation from an FDA panel, which said the evidence wasn't strong enough to support the experimental drug.
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That is, the system will only consider ratings from normal users when computing predictions. The rest of the algorithm works exactly same as normal item-based collaborative filtering. According to experimental results on MovieLens data, this robust CF approach preserves accuracy compared to normal item-based CF, but is more stable.
The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings without any other information about the users or films, i.e. without the users being identified except by numbers assigned for the contest.