3,330 research outputs found
Thompson Sampling for Bandits with Clustered Arms
We propose algorithms based on a multi-level Thompson sampling scheme, for the stochastic multi-armed bandit and its contextual variant with linear expected rewards, in the setting where arms are clustered. We show, both theoretically and empirically, how exploiting a given cluster structure can significantly improve the regret and computational cost compared to using standard Thompson sampling. In the case of the stochastic multi-armed bandit we give upper bounds on the expected cumulative regret showing how it depends on the quality of the clustering. Finally, we perform an empirical evaluation showing that our algorithms perform well compared to previously proposed algorithms for bandits with clustered arms
Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms
Contextual bandit algorithms have become popular for online recommendation
systems such as Digg, Yahoo! Buzz, and news recommendation in general.
\emph{Offline} evaluation of the effectiveness of new algorithms in these
applications is critical for protecting online user experiences but very
challenging due to their "partial-label" nature. Common practice is to create a
simulator which simulates the online environment for the problem at hand and
then run an algorithm against this simulator. However, creating simulator
itself is often difficult and modeling bias is usually unavoidably introduced.
In this paper, we introduce a \emph{replay} methodology for contextual bandit
algorithm evaluation. Different from simulator-based approaches, our method is
completely data-driven and very easy to adapt to different applications. More
importantly, our method can provide provably unbiased evaluations. Our
empirical results on a large-scale news article recommendation dataset
collected from Yahoo! Front Page conform well with our theoretical results.
Furthermore, comparisons between our offline replay and online bucket
evaluation of several contextual bandit algorithms show accuracy and
effectiveness of our offline evaluation method.Comment: 10 pages, 7 figures, revised from the published version at the WSDM
2011 conferenc
Enhancing Evolutionary Conversion Rate Optimization via Multi-armed Bandit Algorithms
Conversion rate optimization means designing web interfaces such that more
visitors perform a desired action (such as register or purchase) on the site.
One promising approach, implemented in Sentient Ascend, is to optimize the
design using evolutionary algorithms, evaluating each candidate design online
with actual visitors. Because such evaluations are costly and noisy, several
challenges emerge: How can available visitor traffic be used most efficiently?
How can good solutions be identified most reliably? How can a high conversion
rate be maintained during optimization? This paper proposes a new technique to
address these issues. Traffic is allocated to candidate solutions using a
multi-armed bandit algorithm, using more traffic on those evaluations that are
most useful. In a best-arm identification mode, the best candidate can be
identified reliably at the end of evolution, and in a campaign mode, the
overall conversion rate can be optimized throughout the entire evolution
process. Multi-armed bandit algorithms thus improve performance and reliability
of machine discovery in noisy real-world environments.Comment: The Thirty-First Innovative Applications of Artificial Intelligence
Conferenc
Context Attentive Bandits: Contextual Bandit with Restricted Context
We consider a novel formulation of the multi-armed bandit model, which we
call the contextual bandit with restricted context, where only a limited number
of features can be accessed by the learner at every iteration. This novel
formulation is motivated by different online problems arising in clinical
trials, recommender systems and attention modeling. Herein, we adapt the
standard multi-armed bandit algorithm known as Thompson Sampling to take
advantage of our restricted context setting, and propose two novel algorithms,
called the Thompson Sampling with Restricted Context(TSRC) and the Windows
Thompson Sampling with Restricted Context(WTSRC), for handling stationary and
nonstationary environments, respectively. Our empirical results demonstrate
advantages of the proposed approaches on several real-life datasetsComment: IJCAI 201
- …