Juan M. Fernández-Luna

Ant colony optimization for learning Bayesian networks

One important approach to learning Bayesian networks (BNs) from data uses a scoring metric to evaluate the fitness of any given candidate network for the data base, and applies a search procedure to explore the set of candidate networks. The most usual search methods are greedy hill climbing, either deterministic or stochastic, although other techniques have also been used. In this paper we propose a new algorithm for learning BNs based on a recently introduced metaheuristic, which has been successfully applied to solve a variety of combinatorial optimization problems: ant colony optimization (ACO). We describe all the elements necessary to tackle our learning problem using this metaheuristic, and experimentally compare the performance of our ACO-based algorithm with other algorithms used in the literature. The experimental work is carried out using three different domains: ALARM, INSURANCE and BOBLO.

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 comparison of learning algorithms for Bayesian networks: a case study based on data from an emergency medical service

Due to the uncertainty of many of the factors that influence the performance of an emergency medical service, we propose using Bayesian networks to model this kind of system. We use different algorithms for learning Bayesian networks in order to build several models, from the hospital managers point of view, and apply them to the specific case of the emergency service of a Spanish hospital. This first study of a real problem includes preliminary data processing, the experiments carried out, the comparison of the algorithms from different perspectives, and some potential uses of Bayesian networks for management problems in the health service.

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.

Teaching and learning in information retrieval

A literature review of pedagogical methods for teaching and learning information retrieval is presented. From the analysis of the literature a taxonomy was built and it is used to structure the paper. Information Retrieval (IR) is presented from different points of view: technical levels, educational goals, teaching and learning methods, assessment and curricula. The review is organized around two levels of abstraction which form a taxonomy that deals with the different aspects of pedagogy as applied to information retrieval. The first level looks at the technical level of delivering information retrieval concepts, and at the educational goals as articulated by the two main subject domains where IR is delivered: computer science (CS) and library and information science (LIS). The second level focuses on pedagogical issues, such as teaching and learning methods, delivery modes (classroom, online or e-learning), use of IR systems for teaching, assessment and feedback, and curricula design. The survey, and its bibliography, provides an overview of the pedagogical research carried out in the field of IR. It also provides a guide for educators on approaches that can be applied to improving the student learning experiences.

Using context information in structured document retrieval: an approach based on influence diagrams

In this paper we present an Information Retrieval System (IRS) which is able to work with structured document collections. The model is based on the influence diagrams formalism: a generalization of Bayesian Networks that provides a visual representation of a decision problem. These offer an intuitive way to identify and display the essential elements of the domain (the structured document components and their usefulness) and also how these are related to each other. They have also associated quantitative knowledge that measures the strength of the interactions. By means of this approach, we shall present structured retrieval as a decision-making problem. Two different models have been designed: SID (Simple Influence Diagram) and CID (Context-based Influence Diagram). The main difference between these two models is that the latter also takes into account influences provided by the context in which each structural component is located.