Diego Fernández

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The technique of collaborative filtering is especially successful in generating personalized recommendations. More than a decade of research has resulted in numerous algorithms, although no comparison of the different strategies has been made. In fact, a universally accepted way of evaluating a collaborative filtering algorithm does not exist yet. In this(More)
The paper describes how virtualization tools can be used in computer network laboratories to simplify and dramatically reduce its deployment and management costs. In particular, the Virtual Network User Mode Linux (VNUML) free-software tool (developed as part of the Euro6IX 1ST research project) is introduced, showing how it can be used to easily build(More)
The performance evaluation of an IR system is a key point in the development of any search engine, and specially in the Web. In order to get the performance we are used to, Web search engines are based on large-scale distributed systems and to optimise its performance is an important aspect in the literature. The main methods, that can be found in the(More)
The recommendation of queries, known as query suggestion, is a common practice on major Web Search Engines. It aims to help users to find the information they are looking for, and is usually based on the knowledge learned from past interactions with the search engine. In this paper we propose a new model for query suggestion, the Search Shortcut Problem,(More)
A Web Information Retrieval course is quite appealing to Computer Science students and is quite challenging from the teacher’s perspective, due to the limited knowledge of IR and Web IR of the students. In this paper we present our experience teaching a mainly practical Web IR course in order to exploit the programming skills of the common Computer Science(More)
Collaborative filtering is one of the most popular recommendation techniques. While the quality of the recommendations has been significantly improved in the last years, most approaches present poor efficiency and scalability. In this paper, we study several factors that affect the performance of a k-Nearest Neighbors algorithm, and we propose a distributed(More)
Collaborative filtering is a popular recommendation technique. Although researchers have focused on the accuracy of the recommendations, real applications also need efficient algorithms. An index structure can be used to store the rating matrix and compute recommendations very fast. In this paper we study how compression techniques can reduce the size of(More)
In the last years, recommender systems have achieved a great popularity. Many different techniques have been developed and applied to this field. However, in many cases the algorithms do not obtain the expected results. In particular, when the applied model does not fit the real data the results are especially bad. This happens because many times models are(More)