The basic objective of a predictive algorithm for collaborative filtering (CF) is to suggest items to a particular user based on his/her preferences and other users with similar interests. Many algorithms have been proposed for CF, and some works comparing sub-sets of them can be found in the literature; however, more comprehensive comparisons are not available. In this work, a meaningful sample of CF algorithms widely reported in the literature were chosen for analysis; they represent different stages in the evolutive process of CF, starting from simple user correlations, going through online learning, up to methods which use classification techniques. Our main purpose is to compare these algorithms when applied on multi-valued ratings.Experiments were conducted on three well-known datasets with different characteristics, using two protocols and four evaluation metrics, representing coverage, accuracy, reliability and agreement of predictions with respect to real values. Results from such experiments showed that the memorybased method is a good option because its results are more precise and reliable compared with the other methods. Online Learning methods exhibit a good level of accuracy with low variation, which makes them reliable models. On the other hand, Support Vector Machines generate predictions with acceptable agreement; however, their accuracy depends on the characteristics of the input data. Finally, DepenPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.
The amount of information currently available, the different media and presentation formats joined with the little time availability of the researchers and people in general, make necessary the implementation of automated tools selecting and evaluating information, aiming at not only optimizing resources but also obtaining useful and personalized results that optimize the daily work of its users. A technique known as Information Filtering could be seen as a solution to this problem. Within an information filtering system, a user introduces a profile in the System which represents his/her information needs; then, the system works to display the relevant information. This article contains a sample of the research carried out by us in this important area, focusing the work towards two of its most representative techniques: "Content Based filtering" and "Collaborative filtering." These techniques have been studied from different points of view, allowing to create a solid framework which involves the necessary criteria for designing and creating a tool using the most outstanding characteristics of each technique. They provide a view to facilitate the work of people devoted to the search, depuration and distribution of information.
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