# Style Conditioned Recommendations

## TL;DR

This paper introduces Style Conditioned Recommendations (SCR), a method that uses style injection via CVAE to diversify recommendations while maintaining relevance, showing significant improvements in predictive metrics and style diversity.

## Contribution

The paper presents a novel semi-supervised approach using style injection with CVAE to enhance recommendation diversity and interpretability of user profiles.

## Key findings

- 12% improvement in NDCG@20 over traditional VAE
- 22% average improvement in AUC for style prediction
- +133% increase in style presence after injection

## Abstract

We propose Style Conditioned Recommendations (SCR) and introduce style injection as a method to diversify recommendations. We use Conditional Variational Autoencoder (CVAE) architecture, where both the encoder and decoder are conditioned on a user profile learned from item content data. This allows us to apply style transfer methodologies to the task of recommendations, which we refer to as injection. To enable style injection, user profiles are learned to be interpretable such that they express users' propensities for specific predefined styles. These are learned via label-propagation from a dataset of item content, with limited labeled points. To perform injection, the condition on the encoder is learned while the condition on the decoder is selected per explicit feedback. Explicit feedback can be taken either from a user's response to a style or interest quiz, or from item ratings. In the absence of explicit feedback, the condition at the encoder is applied to the decoder. We show a 12% improvement on NDCG@20 over the traditional VAE based approach and an average 22% improvement on AUC across all classes for predicting user style profiles against our best performing baseline. After injecting styles we compare the user style profile to the style of the recommendations and show that injected styles have an average +133% increase in presence. Our results show that style injection is a powerful method to diversify recommendations while maintaining personal relevance. Our main contribution is an application of a semi-supervised approach that extends item labels to interpretable user profiles.