most citedItem Graph Convolution Collaborative Filtering for Inductive Recommendations

6 citations · 8 across the 3 of their papers we have counts for

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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…