Active Learning Meets Optimized Item Selection

Autor:	Kleynhans, Bernard, Wang, Xin, Kadıoğlu, Serdar
Rok vydání:	2021
Předmět:	Computer Science - Information Retrieval Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: IJCAI 2021 Data Science Meets Optimization Workshop (DSO@IJCAI 2021)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2112.03105 Zobrazit plný text záznamu View this record from Arxiv