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Juan F. Huete

Bio: Juan F. Huete is an academic researcher from University of Granada. The author has contributed to research in topics: Bayesian network & Recommender system. The author has an hindex of 19, co-authored 125 publications receiving 2050 citations.


Papers
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Journal ArticleDOI
TL;DR: A number of basic operations necessary to develop a calculus with probability intervals, such as combination, marginalization, conditioning and integration are studied in detail.
Abstract: We study probability intervals as an interesting tool to represent uncertain information. A number of basic operations necessary to develop a calculus with probability intervals, such as combination, marginalization, conditioning and integration are studied in detail. Moreover, probability intervals are compared with other uncertainty theories, such as lower and upper probabilities, Choquet capacities of order two and belief and plausibility functions. The advantages of probability intervals with respect to these formalisms in computational efficiency are also highlighted.

305 citations

Journal ArticleDOI
TL;DR: A new Bayesian network model is presented to deal with the problem of hybrid recommendation by combining content-based and collaborative features and is equipped with a flexible topology and efficient mechanisms to estimate the required probability distributions so that probabilistic inference may be performed.
Abstract: Recommender systems enable users to access products or articles that they would otherwise not be aware of due to the wealth of information to be found on the Internet. The two traditional recommendation techniques are content-based and collaborative filtering. While both methods have their advantages, they also have certain disadvantages, some of which can be solved by combining both techniques to improve the quality of the recommendation. The resulting system is known as a hybrid recommender system. In the context of artificial intelligence, Bayesian networks have been widely and successfully applied to problems with a high level of uncertainty. The field of recommendation represents a very interesting testing ground to put these probabilistic tools into practice. This paper therefore presents a new Bayesian network model to deal with the problem of hybrid recommendation by combining content-based and collaborative features. It has been tailored to the problem in hand and is equipped with a flexible topology and efficient mechanisms to estimate the required probability distributions so that probabilistic inference may be performed. The effectiveness of the model is demonstrated using the MovieLens and IMDB data sets.

301 citations

Journal ArticleDOI
TL;DR: This work proposes a system that allows students to interact with physical surrounding objects which are virtualy associated with a subject of learning, and conducts an experimental validation of the approach, yielding evidence that the model improves the student's learning outcomes.
Abstract: The Internet of Things is a new paradigm that is revolutionizing computing. It is intended that all objects around us are connected to the network, providing “anytime, anywhere” access to information. This concept is gaining ground, thanks to advances in nanotechnology which allows the creation of devices capable of connecting to the Internet efficiently. Nowdays a large number of devices are connected to the web, ranging from mobile devices to appliances. In this paper we focus on the education field, where Internet of Things can be used to create more significant learning spaces. In this sense, we propose a system that allows students to interact with physical surrounding objects which are virtualy associated with a subject of learning. We conduct an experimental validation of our approach, yielding evidence that our model improves the student's learning outcomes.

140 citations

Journal ArticleDOI
TL;DR: The proposed algorithm avoids some of the drawbacks of this approach by making an intensive use of low order conditional independence tests, and uses the set of zero- and first-order independence statements in order to obtain a prior skeleton of the network.
Abstract: In the paper we describe a new independence-based approach for learning Belief Networks. The proposed algorithm avoids some of the drawbacks of this approach by making an intensive use of low order conditional independence tests. Particularly, the set of zero- and first-order independence statements are used in order to obtain a prior skeleton of the network, and also to fix and remove arrows from this skeleton. Then, a refinement procedure, based on minimum cardinality d-separating sets, which uses a small number of conditional independence tests of higher order, is carried out to produce the final graph. Our algorithm needs an ordering of the variables in the model as the input. An algorithm that partially overcomes this problem is also presented. ” 2000

119 citations

Journal ArticleDOI
TL;DR: This introductory paper presents a short bibliographical review of the works which have applied Bayesian networks to IR and briefly describes the papers in this special issue which give a good clue about some of the new trends in the area of the application of Bayesian Networks to IR.
Abstract: Bayesian networks, which nowadays constitute the dominant approach for managing probability within the field of Artificial Intelligence, have been applied to Information Retrieval (IR) in different ways during the last 15 years, to solve a wide range of problems where uncertainty is an important feature. In this introductory paper, we first present a short bibliographical review of the works which have applied Bayesian networks to IR. The objective is not to show every approach thoroughly, but rather to provide a brief guide for those researchers who wish to start studying this area.Second, we briefly describe the papers in this special issue, which give a good clue about some of the new trends in the area of the application of Bayesian networks to IR.

72 citations


Cited by
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Journal ArticleDOI
TL;DR: An overview of recommender systems as well as collaborative filtering methods and algorithms is provided, which explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.
Abstract: Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

2,639 citations

Dissertation
01 Jan 1975

2,119 citations

01 Jan 1995
TL;DR: In this paper, the authors propose a method to improve the quality of the data collected by the data collection system. But it is difficult to implement and time consuming and computationally expensive.
Abstract: 本文对国际科学计量学杂志《Scientometrics》1979-1991年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析,揭示出科学计量学研究的重点、活动的中心及发展趋势,说明了学科带头人在发展科学计量学这门新兴学科中的作用。

1,636 citations