81 research outputs found
AdaptEx: A Self-Service Contextual Bandit Platform
This paper presents AdaptEx, a self-service contextual bandit platform widely
used at Expedia Group, that leverages multi-armed bandit algorithms to
personalize user experiences at scale. AdaptEx considers the unique context of
each visitor to select the optimal variants and learns quickly from every
interaction they make. It offers a powerful solution to improve user
experiences while minimizing the costs and time associated with traditional
testing methods. The platform unlocks the ability to iterate towards optimal
product solutions quickly, even in ever-changing content and continuous "cold
start" situations gracefully
Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity
Relevance ranking and result diversification are two core areas in modern
recommender systems. Relevance ranking aims at building a ranked list sorted in
decreasing order of item relevance, while result diversification focuses on
generating a ranked list of items that covers a broad range of topics. In this
paper, we study an online learning setting that aims to recommend a ranked list
with items that maximizes the ranking utility, i.e., a list whose items are
relevant and whose topics are diverse. We formulate it as the cascade hybrid
bandits (CHB) problem. CHB assumes the cascading user behavior, where a user
browses the displayed list from top to bottom, clicks the first attractive
item, and stops browsing the rest. We propose a hybrid contextual bandit
approach, called CascadeHybrid, for solving this problem. CascadeHybrid models
item relevance and topical diversity using two independent functions and
simultaneously learns those functions from user click feedback. We conduct
experiments to evaluate CascadeHybrid on two real-world recommendation
datasets: MovieLens and Yahoo music datasets. Our experimental results show
that CascadeHybrid outperforms the baselines. In addition, we prove theoretical
guarantees on the -step performance demonstrating the soundness of
CascadeHybrid
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