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…