Scalable Bayesian Preference Learning for Crowds
This addresses the challenge of noisy and sparse preference data for crowds, with incremental improvements in scalability for large datasets.
The paper tackles the problem of predicting individual preferences and crowd consensus from sparse pairwise labels by proposing a scalable Bayesian method combining matrix factorization and Gaussian processes, showing competitive performance on recommendation tasks and state-of-the-art improvements on an NLP task with thousands of users and items.
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.