29 citations · 39 across the 10 of their papers we have counts for
10 papers
Sports center customer segmentation: a case study
Juan Soto, Ramón Carmenaty, Miguel Lastra +2
Customer segmentation is a fundamental process to develop effective marketing strategies, personalize customer experience and boost their retention and loyalty. This problem has be…
Predicting IR Personalization Performance using Pre-retrieval Query Predictors
Eduardo Vicente-López, Luis M. de Campos, Juan M. Fernández-Luna +1
Personalization generally improves the performance of queries but in a few cases it may also harms it. If we are able to predict and therefore to disable personalization for those…
On the selection of the correct number of terms for profile construction: theoretical and empirical analysis
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete
In this paper, we examine the problem of building a user profile from a set of documents. This profile will consist of a subset of the most representative terms in the documents th…
Positive unlabeled learning for building recommender systems in a parliamentary setting
Luis M. de Camposa, Juan M. Fernández-Luna, Juan F. Huete +1
Our goal is to learn about the political interests and preferences of the Members of Parliament by mining their parliamentary activity, in order to develop a recommendation/filteri…
Automatic Construction of Multi-faceted User Profiles using Text Clustering and its Application to Expert Recommendation and Filtering Problems
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete +1
In the information age we are living in today, not only are we interested in accessing multimedia objects such as documents, videos, etc. but also in searching for professional exp…
LDA-based Term Profiles for Expert Finding in a Political Setting
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete +1
A common task in many political institutions (i.e. Parliament) is to find politicians who are experts in a particular field. In order to tackle this problem, the first step is to o…