A Bayesian Approach toward Active Learning for Collaborative Filtering
This work addresses the challenge of improving recommendation accuracy with limited user feedback, which is incremental by building on prior active learning methods.
The paper tackles the problem of active learning for collaborative filtering by proposing a Bayesian approach that considers the posterior distribution of the estimated model, leading to more robust performance when user ratings are limited. Empirical results on movie rating datasets show that this method achieves substantially better performance compared to methods based only on the estimated model.
Collaborative filtering is a useful technique for exploiting the preference patterns of a group of users to predict the utility of items for the active user. In general, the performance of collaborative filtering depends on the number of rated examples given by the active user. The more the number of rated examples given by the active user, the more accurate the predicted ratings will be. Active learning provides an effective way to acquire the most informative rated examples from active users. Previous work on active learning for collaborative filtering only considers the expected loss function based on the estimated model, which can be misleading when the estimated model is inaccurate. This paper takes one step further by taking into account of the posterior distribution of the estimated model, which results in more robust active learning algorithm. Empirical studies with datasets of movie ratings show that when the number of ratings from the active user is restricted to be small, active learning methods only based on the estimated model don't perform well while the active learning method using the model distribution achieves substantially better performance.