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5 results on '"Hernando, Antonio"'

1. Hierarchical graph maps for visualization of collaborative recommender systems.

Author

Hernando, Antonio, Moya, Ricardo, Ortega, Fernando, and Bobadilla, Jesús

Subjects
*INFORMATION filtering, *RECOMMENDER systems, *VISUAL perception, *DATABASES, *INFORMATION retrieval
Abstract

In this paper we provide a method that allows the visualization of similarity relationships present between items of collaborative filtering recommender systems, as well as the relative importance of each of these. The objective is to offer visual representations of the recommender system’s set of items and of their relationships; these graphs show us where the most representative information can be found and which items are rated in a more similar way by the recommender system’s community of users. The visual representations achieved take the shape of phylogenetic trees, displaying the numerical similarity and the reliability between each pair of items considered to be similar. As a case study we provide the results obtained using the public database Movielens 1M, which contains 3900 movies. [ABSTRACT FROM PUBLISHER]

Published
2014
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2. Trees for explaining recommendations made through collaborative filtering.

Author

Hernando, Antonio, Bobadilla, Jesús, Ortega, Fernando, and Gutiérrez, Abraham

Subjects
*TREE graphs, *INFORMATION filtering, *RECOMMENDER systems, *DATA visualization, *RELIABILITY in engineering, *INFORMATION technology
Abstract

Abstract: In this paper, we present a novel technique for explaining the recommendations made by recommender systems based on collaborative filtering. Our technique is based on the visualisation of trees of items, and it provides users with a quick and attractive way of understanding the recommendations. This type of visualisation provides users with valuable information about the reliability of the recommendations and the importance of the ratings the user has made, which may help users to decide which recommendation to choose. [Copyright &y& Elsevier]

Published
2013
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3. Incorporating reliability measurements into the predictions of a recommender system

Author

Hernando, Antonio, Bobadilla, Jesús, Ortega, Fernando, and Tejedor, Jorge

Subjects
*RECOMMENDER systems, *COLLECTIVE action, *INFORMATION filtering, *ARBITRARY constants, *INCORPORATION, *INFORMATION science, *RELIABILITY in engineering, *INFORMATION filtering systems
Abstract

Abstract: In this paper we introduce the idea of using a reliability measure associated to the predictions made by recommender systems based on collaborative filtering. This reliability measure is based on the usual notion that the more reliable a prediction, the less liable to be wrong. Here we will define a general reliability measure suitable for any arbitrary recommender system. We will also show a method for obtaining specific reliability measures specially fitting the needs of different specific recommender systems. [Copyright &y& Elsevier]

Published
2013
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4. Collaborative filtering based on significances

Author

Bobadilla, Jesús, Hernando, Antonio, Ortega, Fernando, and Gutiérrez, Abraham

Subjects
*INFORMATION filtering, *FILTERING software, *MP3 players, *ELECTRONIC commerce, *NEWS websites, *TELEVISION programs
Abstract

Abstract: It seems reasonable to think that there may be some items and some users in a recommender system that could be highly significant in making recommendations. For instance, the recent and much-advertised Apple product may be regarded as more significant compared with an outdated MP3 device (which is still on sale). In this paper, we introduce a new method to improve the information used in collaborative filtering processes by weighting the ratings of the items according to their importance. We provide here a formalisation of the collaborative filtering process based on the concept of significance. In this way, the k-neighbours are calculated taking into account the ratings of the items, the significance of the items and the significance of each user for making recommendations to other users. This formalisation includes extensions of the concepts related to similarity measures and prediction/recommendation quality measures. We will show also the results obtained from a set of experiments using Movielens and Netflix. The results confirm the advantage of introducing the concept of significance in general recommender systems and especially in recommender systems in which it is easy to determine the relative importance of the items: for example, most widely sold products in e-commerce, most widely commented news items in web-news, most widely watched programs on TV, and the latest sports champions. [Copyright &y& Elsevier]

Published
2012
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5. Generalization of recommender systems: Collaborative filtering extended to groups of users and restricted to groups of items

Author

Bobadilla, Jesús, Ortega, Fernando, Hernando, Antonio, and Bernal, Jesús

Subjects
*RECOMMENDER systems, *INFORMATION filtering, *GENERALIZATION, *DATABASES, *CONSUMER preferences, *PREDICTION theory, *COMPARATIVE studies
Abstract

Abstract: In this paper we present a collaborative filtering method which opens up the possibilities of traditional collaborative filtering in two aspects: (1) it enables joint recommendations to groups of users and (2) it enables the recommendations to be restricted to items similar to a set of reference items. By way of example, a group of four friends could request joint recommendations of films similar to “Avatar” or “Titanic”. In the paper, using experiments, we show that the traditional approach of collaborative filtering does not satisfactorily resolve the new possibilities contemplated; we also provide a detailed formulation of the method proposed and an extensive set of experiments and comparative results which show the superiority of designed collaborative filtering compared to traditional collaborative filtering in: (a) number of recommendations obtained, (b) quality of the predictions, (c) quality of the recommendations. The experiments have been carried out on the databases Movielens and Netflix. [Copyright &y& Elsevier]

Published
2012
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