Scalable Bayesian Preference Learning for Crowds

TL;DR

This paper introduces a scalable Bayesian preference learning method that combines matrix factorisation and Gaussian processes to predict individual and crowd preferences from pairwise labels, handling noisy and sparse data efficiently.

Contribution

It presents a novel scalable Bayesian approach using stochastic variational inference that integrates input features for preference prediction, outperforming previous methods in large-scale settings.

Findings

Competitive with previous approaches on recommendation tasks

Demonstrates scalability on NLP tasks with thousands of users and items

Shows improvements over the state of the art

Abstract

We propose a scalable Bayesian preference learning method for jointly predicting the preferences of individuals as well as the consensus of a crowd from pairwise labels. Peoples' opinions often differ greatly, making it difficult to predict their preferences from small amounts of personal data. Individual biases also make it harder to infer the consensus of a crowd when there are few labels per item. We address these challenges by combining matrix factorisation with Gaussian processes, using a Bayesian approach to account for uncertainty arising from noisy and sparse data. Our method exploits input features, such as text embeddings and user metadata, to predict preferences for new items and users that are not in the training set. As previous solutions based on Gaussian processes do not scale to large numbers of users, items or pairwise labels, we propose a stochastic variational inference approach that limits computational and memory costs. Our experiments on a recommendation task show that our method is competitive with previous approaches despite our scalable inference approximation. We demonstrate the method's scalability on a natural language processing task with thousands of users and items, and show improvements over the state of the art on this task. We make our software publicly available for future work.