4 research outputs found
Gradient-based Optimization for Bayesian Preference Elicitation
Effective techniques for eliciting user preferences have taken on added
importance as recommender systems (RSs) become increasingly interactive and
conversational. A common and conceptually appealing Bayesian criterion for
selecting queries is expected value of information (EVOI). Unfortunately, it is
computationally prohibitive to construct queries with maximum EVOI in RSs with
large item spaces. We tackle this issue by introducing a continuous formulation
of EVOI as a differentiable network that can be optimized using gradient
methods available in modern machine learning (ML) computational frameworks
(e.g., TensorFlow, PyTorch). We exploit this to develop a novel, scalable Monte
Carlo method for EVOI optimization, which is more scalable for large item
spaces than methods requiring explicit enumeration of items. While we emphasize
the use of this approach for pairwise (or k-wise) comparisons of items, we also
demonstrate how our method can be adapted to queries involving subsets of item
attributes or "partial items," which are often more cognitively manageable for
users. Experiments show that our gradient-based EVOI technique achieves
state-of-the-art performance across several domains while scaling to large item
spaces.Comment: To appear in the Thirty-Fourth AAAI Conference on Artificial
Intelligence (AAAI-20