Work2014

Ensemble contextual bandits for personalized recommendation

Liang Tang · Yexi Jiang · Lei Li · Tao Li

98 citations0/0 files storedACM Conference on Recommender Systems (RecSys)

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Advanced Bandit Algorithms Research Recommender Systems and Techniques Reinforcement Learning in Robotics

Abstract

The cold-start problem has attracted extensive attention among various online services that provide personalized recommendation. Many online vendors employ contextual bandit strategies to tackle the so-called exploration/exploitation dilemma rooted from the cold-start problem. However, due to high-dimensional user/item features and the underlying characteristics of bandit policies, it is often difficult for service providers to obtain and deploy an appropriate algorithm to achieve acceptable and robust economic profit.

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References

2002The Nonstochastic Multiarmed Bandit Problem 2011An Empirical Evaluation of Thompson Sampling 2005Multi-armed Bandit Algorithms and Empirical Evaluation 2011Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms 2010A modern Bayesian look at the multi‐armed bandit 2007The Epoch-Greedy algorithm for contextual multi-armed bandits 2008Online Models for Content Optimization

Cited by

2020Carousel Personalization in Music Streaming Apps with Contextual Bandits 2023Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multiarmed Bandit Approach 2023Unpacking the exploration–exploitation tradeoff on Snapchat: The relationships between users’ exploration–exploitation interests and server log data

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