Juan F. Huete

PROBABILITY INTERVALS: A TOOL FOR UNCERTAIN REASONING

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.

Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks

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.

A new approach for learning belief networks using independence criteria

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

Independence concepts in possibility theory: part I

Abstract From both a theoretical and a practical point of view, the study of the concept of independence has great importance in any formalism that manages uncertainty. In Independence Concepts in Possibility Theory: Part I (de Campos and Huete, Fuzzy Sets and Systems 103 (1999) 127–152) several independence relationships were proposed, using different comparison criteria between conditional possibility measures, and using Hisdal conditioning as the conditioning operator. In this paper, we follow the same approach, but considering possibility measures as particular cases of consonant plausibility measures and, therefore, using Dempster conditioning instead of Hisdals. We formalize several intuitive ideas to define independence relationships, namely ‘not to modify’, ‘not to gain’ and ‘to obtain similar’ information after conditioning, and study their properties. We also compare the results with the previous ones obtained in Part I using Hisdal conditioning. Finally, the marginal problem, i.e., how to obtain a joint possibility distribution from a set of marginals, and the problem of factorizing large possibility distributions, in terms of its conditionally independent components, are considered.

Bayesian networks and information retrieval: an introduction to the special issue

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.

Managing uncertainty in group recommending processes

AbstractWhile the problem of building recommender systems has attracted considerable attention in recent years, most recommender systems are designed for recommending items to individuals. The aim of this paper is to automatically recommend a ranked list of new items to a group of users. We will investigate the value of using Bayesian networks to represent the different uncertainties involved in a group recommending process, i.e. those uncertainties related to mechanisms that govern the interactions between group members and the processes leading to the final choice or recommendation. We will also show how the most common aggregation strategies might be encoded using a Bayesian network formalism. The proposed model can be considered as a collaborative Bayesian network-based group recommender system, where group ratings are computed from the past voting patterns of other users with similar tastes.

The BNR model: foundations and performance of a Bayesian network-based retrieval model

This paper presents an information retrieval model based on the Bayesian network formalism. The topology of the network (representing the dependence relationships between terms and documents) as well as the quantitative knowledge (the probabilities encoding the strength of these relationships) will be mined from the document collection using automatic learning algorithms. The relevance of a document to a given query is obtained by means of an inference process through a complex network of dependences. A new inference technique, called propagation + evaluation, has been developed in order to obtain the exact probabilities of relevance in the whole network efficiently.

A collaborative recommender system based on probabilistic inference from fuzzy observations

The problem of building recommender systems has attracted considerable attention in recent years. The objective of this paper is to automatically suggest and rank a list of new items to a user based on the past voting patterns of other users with similar tastes. The proposed model can be considered as a Soft Computing-based collaborative recommender system. The combination of Bayesian networks, which enables an intuitive representation of the mechanisms that govern the relationships between the users, and the Fuzzy Set Theory, enabling us to represent ambiguity or vagueness in the description of the ratings, improves the accuracy of the system.

An Information Retrieval Model Based on Simple Bayesian Networks

In this article a new probabilistic information retrieval (IR) model, based on Bayesian networks (BNs), is proposed. We first consider a basic model, which represents only direct relationships between the documents in the collection and the terms or keywords used to index them. Next, we study two versions of an extended model, which also represents direct relationships between documents. In either case the BNs are used to compute efficiently, by means of a new and exact propagation algorithm, the posterior probabilities of relevance of the documents in the collection given a query. The performance of the proposed retrieval models is tested through a series of experiments with several standard document collections.

Interaction System based on Internet of Things as Support for Education

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 students learning outcomes.