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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…