1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.IR2022
Multi-stage Ensemble Model for Cross-market Recommendation
Cesare Bernardis
This paper describes the solution of our team PolimiRank for the WSDM Cup 2022 on cross-market recommendation. The goal of the competition is to effectively exploit the information…
cs.IR2021★ 1 cited
On the instability of embeddings for recommender systems: the case of Matrix Factorization
Giovanni Gabbolini, Edoardo D'Amico, Cesare Bernardis +1
Most state-of-the-art top-N collaborative recommender systems work by learning embeddings to jointly represent users and items. Learned embeddings are considered to be effective to…
cs.LG2018
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…