# Mixture-of-tastes Models for Representing Users with Diverse Interests

arXiv: https://arxiv.org/abs/1711.08379 · HTML: https://mlsanity.com/paper/1711.08379/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2017.

## Built on by (most cited first, 1 of 1)

- [To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders](https://mlsanity.com/paper/2310.14079/) (2023)

## Abstract (arXiv)

Most existing recommendation approaches implicitly treat user tastes as unimodal, resulting in an average-of-tastes representations when multiple distinct interests are present. We show that appropriately modelling the multi-faceted nature of user tastes through a mixture-of-tastes model leads to large increases in recommendation quality. Our result holds both for deep sequence-based and traditional factorization models, and is robust to careful selection and tuning of baseline models. In sequence-based models, this improvement is achieved at a very modest cost in model complexity, making mixture-of-tastes models a straightforward improvement on existing baselines.