Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. These vulnerabilities mostly emanate from the open nature of such systems ...
Bamshad Mobasher, Robin D. Burke, Chad Williams, R...
Abstract. With the amount of available information on the Web growing rapidly with each day, the need to automatically filter the information in order to ensure greater user effici...
Miha Grcar, Dunja Mladenic, Blaz Fortuna, Marko Gr...
Abstract. To make accurate recommendations, recommendation systems currently require more data about a customer than is usually available. We conjecture that the weaknesses are due...
Redescription mining is a newly introduced data mining problem that seeks to find subsets of data that afford multiple definitions. It can be viewed as a generalization of associa...
Regardless of the frequent patterns to discover, either the full frequent patterns or the condensed ones, either closed or maximal, the strategy always includes the traversal of t...
Learning ranking (or preference) functions has been a major issue in the machine learning community and has produced many applications in information retrieval. SVMs (Support Vect...
Protection of privacy has become an important problem in data mining. In particular, individuals have become increasingly unwilling to share their data, frequently resulting in in...
In this paper, we present a general framework to discover spatial associations and spatio-temporal episodes for scientific datasets. In contrast to previous work in this area, fea...
We address privacy-preserving classification problem in a distributed system. Randomization has been the approach proposed to preserve privacy in such scenario. However, this appr...