DenseRec: Revisiting Dense Content Embeddings for Sequential Transformer-based Recommendation

Jan Malte Lichtenberg, Antonio De Candia, Matteo Ruffini

Transformer-based sequential recommenders, such as SASRec or BERT4Rec, typically rely solely on learned item ID embeddings, making them vulnerable to the item cold-start problem, p…