Active Learning Meets Optimized Item Selection
This work addresses data scarcity in recommendation systems, but it appears incremental as it combines existing techniques like active learning and optimization.
The paper tackles the challenge of limited training data in recommendation systems by formulating a combinatorial optimization problem for optimized item selection to shorten data collection time, integrating discrete optimization, clustering, and embeddings within a multi-level framework.
Designing recommendation systems with limited or no available training data remains a challenge. To that end, a new combinatorial optimization problem is formulated to generate optimized item selection for experimentation with the goal to shorten the time for collecting randomized training data. We first present an overview of the optimized item selection problem and a multi-level optimization framework to solve it. The approach integrates techniques from discrete optimization, unsupervised clustering, and latent text embeddings. We then discuss how to incorporate optimized item selection with active learning as part of randomized exploration in an ongoing fashion.