Andrea Barraza-Urbina

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Recommender Systems (RS) have emerged to guide users in the task of efficiently browsing/exploring a large product space, helping users to quickly identify interesting products. However, suggestions generated with traditional RS usually do not produce diverse results though it has been argued that diversity is a desirable feature. The study of(More)
This paper presents the practical experience and results of the Lion Project, which aimed to improve software development times at Heinsohn Business Technology (HBT), a large-scale Colombian software development company. The main result of this project is the LionWizard Framework, a set of libraries and tools with a focus on large-scale software reuse and(More)
Thanks to new information technologies, users can access a large amount and variety of news stories, anytime and anywhere. Nonetheless, current mechanisms for news dissemination do not properly assist users in spotting news articles that could be potentially interesting for the particular user. Commercial applications are developing solutions to address(More)
Recommender Systems have emerged to help support, augment and systematize the everyday natural social process of creating and sharing recommendations by developing tools that can be used to quickly identify interesting products, and therefore, reduce a search space of alternatives. This paper aims to present a framework, constructed under a generic(More)
This paper describes <b><i>UWIRS</i></b> (acronym of <b><i>U</i></b>biquitous <b><i>W</i></b>eb <b><i>I</i></b>nformation <b><i>R</i></b>etrieval <b><i>S</i></b>olution), an agent-based <b><i>W</i></b>eb <b><i>I</i></b>nformation <b><i>R</i></b>etrieval (<i>WIR</i>) solution designed taking into account the unique features of the <b><i>W</i></b>orld(More)
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