When are recommender systems useful?

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dc.creatorBlattner, Marcel
dc.creatorHunziker, Alexander
dc.creatorLaureti, Paolo
dc.date2007-09-17
dc.date.accessioned2026-07-07T08:30:19Z
dc.date.available2026-07-07T08:30:19Z
dc.descriptionRecommender systems are crucial tools to overcome the information overload brought about by the Internet. Rigorous tests are needed to establish to what extent sophisticated methods can improve the quality of the predictions. Here we analyse a refined correlation-based collaborative filtering algorithm and compare it with a novel spectral method for recommending. We test them on two databases that bear different statistical properties (MovieLens and Jester) without filtering out the less active users and ordering the opinions in time, whenever possible. We find that, when the distribution of user-user correlations is narrow, simple averages work nearly as well as advanced methods. Recommender systems can, on the other hand, exploit a great deal of additional information in systems where external influence is negligible and peoples' tastes emerge entirely. These findings are validated by simulations with artificially generated data.
dc.identifierhttps://arxiv.org/abs/0709.2562
dc.identifierhttp://arxiv.org/abs/0709.2562
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/138213
dc.subjectInformation Retrieval
dc.subjectComputers and Society
dc.subjectDigital Libraries
dc.subjectData Structures and Algorithms
dc.subjectData Analysis, Statistics and Probability
dc.subjectPhysics and Society
dc.titleWhen are recommender systems useful?
dc.typetext

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