Reward Optimizing Recommender Systems
Machine Learning Methods for Optimizing Business Metrics Observed at A/B Test Time
Buch in deiner Nähe kaufen
Beschreibung
Readers of recent books and recommender systems papers will learn a great deal about a plethora of machine learning techniques used in recommender systems including collaborative filtering, content based recommender systems, click models, off policy estimation, reinforcement learning and contextual bandits. They will also learn about A/B testing, but it will be somewhat an afterthought.
This book is different, it makes optimizing for reward at A/B test time the central point of study. The methodology of A/B testing is described in detail, highlighting both its significant strengths and potential limitations. The remainder of the book is developed from this point of view. This foundational nature will require a more comprehensive discussion of inferential and causal theory than other treatments, providing a stronger understanding about how the disparate machine learning techniques for recommendation fit together.
Until now, it has required a great deal of industry experience to identify why approaches based on (say) a direct implementation of reinforcement learning are likely to fail in production. Readers of this book should gain this intuition with less need to make painful mistakes in the wild.
Readers of recent books and recommender systems papers will learn a great deal about a plethora of machine learning techniques used in recommender systems including collaborative filtering, content based recommender systems, click models, off policy estimation, reinforcement learning and contextual bandits. They will also learn about A/B testing, but it will be somewhat an afterthought.
This book is different, it makes optimizing for reward at A/B test time the central point of study. The methodology of A/B testing is described in detail, highlighting both its significant strengths and potential limitations. The remainder of the book is developed from this point of view. This foundational nature will require a more comprehensive discussion of inferential and causal theory than other treatments, providing a stronger understanding about how the disparate machine learning techniques for recommendation fit together.
Until now, it has required a great deal of industry experience to identify why approaches based on (say) a direct implementation of reinforcement learning are likely to fail in production. Readers of this book should gain this intuition with less need to make painful mistakes in the wild.
This book makes optimizing for reward at A/B test time the central point of study The methodology of A/B testing is described in detail Readers gain a stronger understanding of how the disparate machine learning techniques for recommendation fit together
Autor*in
David Rohde
Themen in »Reward Optimizing Recommender Systems«
Recommender Systems Information Retrieval Session-Based Recommender Systems Collaborative Filtering Decision Theory Bayesian Probability Click Models for Recommendation Reinforcement Learning