The outlying property detection problem (OPDP) is the problem of discovering the properties distinguishing a given object, known in advance to be an outlier in a database, from the other database objects. This problem has been recently analyzed …
We study social influence from a topic modeling perspective. We introduce novel topic-aware influence-driven propagation models that experimentally result to be more accurate in describing real-world cascades than the standard propagation models …
Recent works in Recommender Systems (RS) have investigated the relationships between the prediction accuracy, i.e. the ability of a RS to minimize a cost function (for instance the RMSE measure) in estimating users' preferences, and the accuracy of …
We propose two models for improving the performance of rule-based classification under unbalanced and highly imprecise domains. Both models are probabilistic frameworks aimed to boost the performance of basic rule-based classifiers. The first model …
This paper presents a hierarchical probabilistic approach to collaborative filtering which allows the discovery and analysis of both global patterns (i.e., tendency of some products of being ‘universally appreciated’) and local patterns (tendency of …
In this work we perform an analysis of probabilistic approaches to recommendation upon a different validation perspective, which focuses on accuracy metrics such as recall and precision of the recommendation list. Traditionally, state-of-art …
We propose a bayesian probabilistic model for explicit preference data. The model introduces a generative process, which takes into account both item selection and rating emission to gather into communities those users who experience the same items …