DSpace Angular :: Browsing LIAAD - Other Publications by Author "Alípio Jorge"

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Accelerating Recommender Systems using GPUs

( 2015) André Valente Rodrigues ; Alípio Jorge ; Inês Dutra

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Binary recommender systems: Introduction, an application and outlook

( 2013) Alípio Jorge

Recommender Systems are a hot application area these days, made popular by well known web sites. The problem of predicting user preferences is very demanding from the data mining algorithm design point of view, but it also poses challenges to evaluation and monitoring. Moreover, there is a lot of information that can be exploited, from clickstreams and background information to musical content and social interaction. As data grows and recommendation requests must be answered in a split second, online and agile solutions must be implemented. In this talk we will give a brief introduction to binary recommender systems, describe a particular hybrid application to music recommendation - from algorithm to online evaluation, and refer to context aware and online recommender algorithms. © 2013 ACM.

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Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems

( 2013) Domingues,MA ; Alípio Jorge ; Carlos Manuel Soares

Traditionally, recommender systems for the web deal with applications that have two dimensions, users and items. Based on access data that relate these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a multidimensional approach, called DaVI (Dimensions as Virtual Items), that consists in inserting contextual and background information as new user-item pairs. The main advantage of this approach is that it can be applied in combination with several existing two-dimensional recommendation algorithms. To evaluate its effectiveness, we used the DaVI approach with two different top-N recommender algorithms, Item-based Collaborative Filtering and Association Rules based, and ran an extensive set of experiments in three different real world data sets. In addition, we have also compared our approach to the previously introduced combined reduction and weight post-filtering approaches. The empirical results strongly indicate that our approach enables the application of existing two-dimensional recommendation algorithms in multidimensional data, exploiting the useful information of these data to improve the predictive ability of top-N recommender systems.

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Evaluation of recommender systems in streaming environments