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cs.IR2025
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…
cs.IR2024
Ranking Across Different Content Types: The Robust Beauty of Multinomial Blending
Jan Malte Lichtenberg, Giuseppe Di Benedetto, Matteo Ruffini
An increasing number of media streaming services have expanded their offerings to include entities of multiple content types. For instance, audio streaming services that started by…