Abstract
Building recommender systems (RSs) has attracted considerable attention in the recent years. The main problem with these systems lies in those items for which we have little information and which cause incorrect predictions. One accredited solution involves using the items’ content information to improve these recommendations, but this cannot be applied in situations where the content information is unavailable. In this paper we present a novel idea to deal with this problem, using only the available users’ ratings. The objective is to use all possible information in the dataset to improve recommendations made with little information. For this purpose we will use what we call second-hand information: in the recommendation process, when a similar user has not rated the target item, we will guess his/her preferences using the information available. This idea is independent from the RS used and, in order to test it, we will employ two different collaborative RS. The results obtained confirm the soundness of our proposal.
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To clarify, we show a recursive version of the algorithm, but the implemented version is sequential.
This is one of the differences from the reference model presented in de Campos et al. (2008), i.e., the inclusion of rating 0 in the performance of the system.
We have evaluated the system with only positive Pearson correlation and we have obtained worst results than using absolute value.
Note that in this mode we do not show the success ratio as error measure because the predicted value is not an ordinal value.
In order to test this fact we have also included all the ratings but it worsen the performance of the systems.
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Acknowledgments
This study has been jointly supported by the Spanish Ministerio de Educación y Ciencia under the projects TIN2005-02516 and TIN2008-06566-C04-01, and the research program Consolider Ingenio 2010 under the project CSD2007-00018.
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de Campos, L.M., Fernández-Luna, J.M., Huete, J.F. et al. Using second-hand information in collaborative recommender systems. Soft Comput 14, 785–798 (2010). https://doi.org/10.1007/s00500-009-0474-5
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DOI: https://doi.org/10.1007/s00500-009-0474-5