584 citations · 1.5k across the 28 of their papers we have counts for
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Deriving item features relevance from collaborative domain knowledge
Maurizio Ferrari Dacrema, Alberto Gasparin, Paolo Cremonesi
An Item based recommender system works by computing a similarity between items, which can exploit past user interactions (collaborative filtering) or item features (content based f…
Eigenvalue analogy for confidence estimation in item-based recommender systems
Maurizio Ferrari Dacrema, Paolo Cremonesi
Item-item collaborative filtering (CF) models are a well known and studied family of recommender systems, however current literature does not provide any theoretical explanation of…
A novel graph-based model for hybrid recommendations in cold-start scenarios
Cesare Bernardis, Maurizio Ferrari Dacrema, Paolo Cremonesi
Cold-start is a very common and still open problem in the Recommender Systems literature. Since cold start items do not have any interaction, collaborative algorithms are not appli…
Sequence-Aware Recommender Systems
Massimo Quadrana, Paolo Cremonesi, Dietmar Jannach
Recommender systems are one of the most successful applications of data mining and machine learning technology in practice. Academic research in the field is historically often bas…
Social Network based Short-Term Stock Trading System
Paolo Cremonesi, Chiara Francalanci, Alessandro Poli +4
This paper proposes a novel adaptive algorithm for the automated short-term trading of financial instrument. The algorithm adopts a semantic sentiment analysis technique to inspect…