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Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms

https://doi.org/10.1145/1935826.1935878
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Bandit Based Monte-Carlo Planning
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Some Aspects of the Sequential Design of Experiments
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Reinforcement Learning with Immediate Rewards and Linear Hypotheses
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Asymptotically efficient adaptive allocation rules
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Reinforcement Learning: An Introduction
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On the method of bounded differences
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Finite-time Analysis of the Multiarmed Bandit Problem
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Associative Reinforcement Learning: Functions in k-DNF
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A One-Armed Bandit Problem with a Concomitant Variable
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Some aspects of the sequential design of experiments
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ON THE LIKELIHOOD THAT ONE UNKNOWN PROBABILITY EXCEEDS ANOTHER IN VIEW OF THE EVIDENCE OF TWO SAMPLES
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Reinforcement Learning:
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Explore/Exploit Schemes for Web Content Optimization
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Bandit problems with side observations
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Reinforcement Learning: An Introduction
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Reinforcement Learning: An Introduction
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Bandit Processes and Dynamic Allocation Indices
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The Nonstochastic Multiarmed Bandit Problem
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PAC model-free reinforcement learning
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Experience-efficient learning in associative bandit problems
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Exploration scavenging
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Spatio-temporal models for estimating click-through rate
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A contextual-bandit approach to personalized news article recommendation
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Online learning for recency search ranking using real-time user feedback
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Sample mean based index policies byO(logn) regret for the multi-armed bandit problem
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Bandit Problems-Sequential Allocation of Experiments.
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Bandit Problems, Sequential Allocation of Experiments.
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Contextual Bandit Algorithms with Supervised Learning Guarantees
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Learning from Logged Implicit Exploration Data
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RCV1: A New Benchmark Collection for Text Categorization Research

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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inferred← citeshttps://doi.org/10.1145/2623372openalex-snapshot
Exploration in Interactive Personalized Music Recommendation
inferred← citeshttps://doi.org/10.1145/2645710.2645732openalex-snapshot
Ensemble contextual bandits for personalized recommendation
inferred← citeshttps://doi.org/10.1145/2645710.2645733openalex-snapshot
Explore-exploit in top-N recommender systems via Gaussian processes
inferred← citeshttps://doi.org/10.1145/2684822.2685311openalex-snapshot
Toward Predicting the Outcome of an A/B Experiment for Search Relevance
inferred← citeshttps://doi.org/10.1145/2736277.2741104openalex-snapshot
Gathering Additional Feedback on Search Results by Multi-Armed Bandits with Respect to Production Ranking
inferred← citeshttps://doi.org/10.1145/2740908.2742562openalex-snapshot
Counterfactual Estimation and Optimization of Click Metrics in Search Engines
inferred← citeshttps://doi.org/10.1145/2740908.2742564openalex-snapshot
Counterfactual Risk Minimization
inferred← citeshttps://doi.org/10.1145/2740908.2742565openalex-snapshot
Unbiased Ranking Evaluation on a Budget
inferred← citeshttps://doi.org/10.1145/2766462.2767707openalex-snapshot
Personalized Recommendation via Parameter-Free Contextual Bandits
inferred← citeshttps://doi.org/10.1145/2783258.2783269openalex-snapshot
Statistical Arbitrage Mining for Display Advertising
inferred← citeshttps://doi.org/10.1145/2783258.2788614openalex-snapshot
Personalizing LinkedIn Feed
inferred← citeshttps://doi.org/10.1145/2792838.2800184openalex-snapshot
Predicting Online Performance of News Recommender Systems Through Richer Evaluation Metrics
inferred← citeshttps://doi.org/10.1145/2835776.2835804openalex-snapshot
Multileave Gradient Descent for Fast Online Learning to Rank
inferred← citeshttps://doi.org/10.1145/2883851.2883868openalex-snapshot
Impact of data collection on interpretation and evaluation of student models
inferred← citeshttps://doi.org/10.1145/2911451.2911500openalex-snapshot
Click-based Hot Fixes for Underperforming Torso Queries
inferred← citeshttps://doi.org/10.1145/2911451.2911528openalex-snapshot
Contextual Bandits in a Collaborative Environment
inferred← citeshttps://doi.org/10.1145/2911451.2911537openalex-snapshot
Learning to Rank with Selection Bias in Personal Search
inferred← citeshttps://doi.org/10.1145/2911451.2914803openalex-snapshot
Counterfactual Evaluation and Learning for Search, Recommendation and Ad Placement
inferred← citeshttps://doi.org/10.1145/2939672.2939713openalex-snapshot
Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising
inferred← citeshttps://doi.org/10.1145/2939672.2939756openalex-snapshot
Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications
inferred← citeshttps://doi.org/10.1145/2939672.2939878openalex-snapshot
Online Context-Aware Recommendation with Time Varying Multi-Armed Bandit
inferred← citeshttps://doi.org/10.1145/2970398.2970410openalex-snapshot
Unbiased Comparative Evaluation of Ranking Functions
inferred← citeshttps://doi.org/10.1145/2983323.2983816openalex-snapshot
Multiple Queries as Bandit Arms
inferred← citeshttps://doi.org/10.1145/2983323.2983847openalex-snapshot
Learning Hidden Features for Contextual Bandits
inferred← citeshttps://doi.org/10.1145/3018661.3018699openalex-snapshot
Unbiased Learning-to-Rank with Biased Feedback
inferred← citeshttps://doi.org/10.1145/3038912.3052656openalex-snapshot
Optimizing the Recency-Relevancy Trade-off in Online News Recommendations
inferred← citeshttps://doi.org/10.1145/3077136.3082066openalex-snapshot
Candidate Selection for Large Scale Personalized Search and Recommender Systems
inferred← citeshttps://doi.org/10.1145/3097983.3098155openalex-snapshot
Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers
inferred← citeshttps://doi.org/10.1145/3124749.3124755openalex-snapshot
Ranking and Calibrating Click-Attributed Purchases in Performance Display Advertising
inferred← citeshttps://doi.org/10.1145/3132847.3132880openalex-snapshot
Interactive Social Recommendation
inferred← citeshttps://doi.org/10.1145/3132847.3133025openalex-snapshot
Returning is Believing
inferred← citeshttps://doi.org/10.1145/3137597.3137601openalex-snapshot
Common Pitfalls in Training and Evaluating Recommender Systems
inferred← citeshttps://doi.org/10.1145/3152434.3152435openalex-snapshot
Harvesting Randomness to Optimize Distributed Systems
inferred← citeshttps://doi.org/10.1145/3152434.3152448openalex-snapshot
Biases in Data-Driven Networking, and What to Do About Them
inferred← citeshttps://doi.org/10.1145/3159652.3159687openalex-snapshot
Offline A/B Testing for Recommender Systems
inferred← citeshttps://doi.org/10.1145/3159652.3159700openalex-snapshot
Short-Term Satisfaction and Long-Term Coverage
inferred← citeshttps://doi.org/10.1145/3159652.3159732openalex-snapshot
Position Bias Estimation for Unbiased Learning to Rank in Personal Search
inferred← citeshttps://doi.org/10.1145/3172944.3172967openalex-snapshot
Ensemble Recommendations via Thompson Sampling
inferred← citeshttps://doi.org/10.1145/3178876.3186165openalex-snapshot
Learning to Collaborate
inferred← citeshttps://doi.org/10.1145/3204461openalex-snapshot
Optimizing Whole-Page Presentation for Web Search
inferred← citeshttps://doi.org/10.1145/3209978.3210051openalex-snapshot
Learning Contextual Bandits in a Non-stationary Environment
inferred← citeshttps://doi.org/10.1145/3219819.3220028openalex-snapshot
Offline Evaluation of Ranking Policies with Click Models
inferred← citeshttps://doi.org/10.1145/3237187openalex-snapshot
A Bandit-Based Ensemble Framework for Exploration/Exploitation of Diverse Recommendation Components
inferred← citeshttps://doi.org/10.1145/3240323.3240344openalex-snapshot
Interactive recommendation via deep neural memory augmented contextual bandits
inferred← citeshttps://doi.org/10.1145/3240323.3240355openalex-snapshot
Unbiased offline recommender evaluation for missing-not-at-random implicit feedback
inferred← citeshttps://doi.org/10.1145/3240323.3240370openalex-snapshot
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility
inferred← citeshttps://doi.org/10.1145/3240323.3241729openalex-snapshot
Artwork personalization at netflix
inferred← citeshttps://doi.org/10.1145/3289600.3290958openalex-snapshot
When People Change their Mind
inferred← citeshttps://doi.org/10.1145/3289600.3291017openalex-snapshot
Estimating Position Bias without Intrusive Interventions
inferred← citeshttps://doi.org/10.1145/3289600.3291027openalex-snapshot
Offline Evaluation to Make Decisions About PlaylistRecommendation Algorithms
inferred← citeshttps://doi.org/10.1145/3292500.3330852openalex-snapshot
Focused Context Balancing for Robust Offline Policy Evaluation
inferred← citeshttps://doi.org/10.1145/3292500.3330864openalex-snapshot
Off-policy Learning for Multiple Loggers
inferred← citeshttps://doi.org/10.1145/3298689.3346956openalex-snapshot
Bandit algorithms in recommender systems
inferred← citeshttps://doi.org/10.1145/3298689.3347018openalex-snapshot
Uplift-based evaluation and optimization of recommenders
inferred← citeshttps://doi.org/10.1145/3298689.3347019openalex-snapshot
Online learning to rank for sequential music recommendation
inferred← citeshttps://doi.org/10.1145/3298689.3347069openalex-snapshot
Revisiting offline evaluation for implicit-feedback recommender systems
inferred← citeshttps://doi.org/10.1145/3306618.3314245openalex-snapshot
Human-AI Learning Performance in Multi-Armed Bandits
inferred← citeshttps://doi.org/10.1145/3308558.3313592openalex-snapshot
Deriving User- and Content-specific Rewards for Contextual Bandits
inferred← citeshttps://doi.org/10.1145/3308558.3313697openalex-snapshot
Addressing Trust Bias for Unbiased Learning-to-Rank
inferred← citeshttps://doi.org/10.1145/3308558.3313727openalex-snapshot
Dynamic Ensemble of Contextual Bandits to Satisfy Users' Changing Interests
inferred← citeshttps://doi.org/10.1145/3308774.3308780openalex-snapshot
What Should We Teach in Information Retrieval?
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