4 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.IR2017★ 4 cited
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…
cs.IR2017
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…