Maria Salamó

Group recommender systems: a critiquing based approach

Group recommender systems introduce a whole set of new challenges for recommender systems research. The notion of generating a set of recommendations that will satisfy a group of users, with potentially competing interests, is challenging in itself. In addition to this we must consider how to record and combine the preferences of many different users as they engage in simultaneous recommendation dialogs. In this paper we introduce a group recommender system that is designed to provide assistance to a group of friends trying to plan a skiing vacation.

Computer aided diagnosis with case-based reasoning and genetic algorithms

This article addresses breast cancer diagnosis using mammographic images. Throughout, the diagnosis is done using the mammographic microcalcifications. The aim of the work presented here is twofold. First, we introduce a back-end phase, based on machine learning techniques, in a previous computer aided diagnosis system. The two machine learning techniques incorporated are case-based reasoning and genetic algorithms. These algorithms look for improving the results obtained by human experts and the previous statistical model. On the other hand, we analyse the obtained results comparing them with the ones provided by other well-known machine learning techniques. The breast cancer dataset used in the experiments come from Girona Health Area. This database contains 216 images previously diagnosed by surgical biopsy.

The needs of the many: a case-based group recommender system

While much of the research in the area of recommender systems has focused on making recommendations to the individual, many recommendation scenarios involve groups of inter-related users. In this paper we consider the challenges presented by the latter scenario. We introduce a (case-based) group recommender designed to meet these challenges through a variety of recommendation features, including the generation of reactive and proactive suggestions based on user feedback in the form of critiques, and demonstrate its effectiveness through a live-user case-study.

Rough Sets Reduction Techniques for Case-Based Reasoning

Case Based Reasoning systems are often faced with the problem of deciding which instances should be stored in the case base. An accurate selection of the best cases could avoid the system being sensitive to noise, having a large memory storage requirements and, having a slow execution speed. This paper proposes two reduction techniques based on Rough Sets theory: Accuracy Rough Sets Case Memory (AccurCM) and Class Rough Sets Case Memory (ClassCM). Both techniques reduce the case base by analysing the representativity of each case of the initial case base and applying a different policy to select the best set of cases. The first one extracts the degree of completeness of our knowledge. The second one obtains the quality of approximation of each case. Experiments using different domains, most of them from the UCI repository, show that the reduction techniques maintain accuracy obtained when not using them. The results obtained are compared with those obtained using well-known reduction techniques.

Knowledge discovery from user preferences in conversational recommendation

Knowledge discovery for personalizing the product recommendation task is a major focus of research in the area of conversational recommender systems to increase efficiency and effectiveness. Conversational recommender systems guide users through a product space, alternatively making product suggestions and eliciting user feedback. Critiquing is a common and powerful form of feedback, where a user can express her feature preferences by applying a series of directional critiques over recommendations, instead of providing specific value preferences. For example, a user might ask for a ‘less expensive’ vacation in a travel recommender; thus ‘less expensive’ is a critique over the price feature. The expectation is that on each cycle, the system discovers more about the user’s soft product preferences from minimal information input. In this paper we describe three different strategies for knowledge discovery from user preferences that improve recommendation efficiency in a conversational system using critiquing. Moreover, we will demonstrate that while the strategies work well separately, their combined effort has the potential to considerably increase recommendation efficiency even further.

Deleting and Building Sort Out Techniques for Case Base Maintenance

Early workon case based reasoning reported in the literature shows the importance of case base maintenance for successful practical systems. Different criteria to the maintenance task have been used for more than half a century. In this paper we present different sort out techniques for case base maintenance. All the sort out techniques proposed are based on the same principle: a Rough Sets competence model. First of all, we present sort out reduction techniques based on deletion of cases. Next, we present sort out techniques that build new reduced competent case memories based on the original ones. The main purpose of these methods is to maintain the competence and reduce, as much as possible, its size. Experiments using different domains, most of them from the UCI repository, show that the reduction techniques maintain the competence obtained by the original case memory. The results are analysed with those obtained using well-known reduction techniques.

Unifying weighting and case reduction methods based on rough sets to improve retrieval

Case-Based Reasoning systems usually retrieve cases using a similarity function based on K-NN or some derivatives. These functions are sensitive to irrelevant or noisy features. Weighting methods are used to extract the most important information present in the knowledge and determine the importance of each feature. However, this knowledge, can also be incorrect, redundant and inconsistent. In order to solve this problem there exist a great number of case reduction techniques in the literature. This paper analyses and justifies the relationship between weighting and case reduction methods, and also analyses their behaviour using different similarity metrics.We have focused this relation on Rough Sets approaches. Several experiments, using different domains from the UCI and our own repository, show that this integration maintain and even improve the performance over a simple CBR system and over case reduction techniques. However, the combined approach produces CBR system decrease if the weighting method declines its performance.

Dynamic Case Base Maintenance for a Case-Based Reasoning System

The success of a case-based reasoning system depends critically on the relevance of the case base. Much current CBR research focuses on how to compact and refine the contents of a case base at two stages, acquisition or learning, along the problem solving process. Although the two stages are closely related, there is few research on using strategies at both stages at the same time. This paper presents a model that allows to update itself dynamically taking information from the learning process. Different policies has been applied to test the model. Several experiments show its effectiveness in different domains from the UCI repository.