136 citations · 168 across the 4 of their papers we have counts for
4 papers
Recommender Systems with Generative Retrieval
Shashank Rajput, Nikhil Mehta, Anima Singh +10
Modern recommender systems perform large-scale retrieval by first embedding queries and item candidates in the same unified space, followed by approximate nearest neighbor search t…
Mixture-of-tastes Models for Representing Users with Diverse Interests
Maciej Kula
Most existing recommendation approaches implicitly treat user tastes as unimodal, resulting in an average-of-tastes representations when multiple distinct interests are present. We…
Binary Latent Representations for Efficient Ranking: Empirical Assessment
Maciej Kula
Large-scale recommender systems often face severe latency and storage constraints at prediction time. These are particularly acute when the number of items that could be recommende…
Metadata Embeddings for User and Item Cold-start Recommendations
Maciej Kula
I present a hybrid matrix factorisation model representing users and items as linear combinations of their content features' latent factors. The model outperforms both collaborativ…