129 research outputs found
Dynamic Ad Allocation: Bandits with Budgets
We consider an application of multi-armed bandits to internet advertising
(specifically, to dynamic ad allocation in the pay-per-click model, with
uncertainty on the click probabilities). We focus on an important practical
issue that advertisers are constrained in how much money they can spend on
their ad campaigns. This issue has not been considered in the prior work on
bandit-based approaches for ad allocation, to the best of our knowledge.
We define a simple, stylized model where an algorithm picks one ad to display
in each round, and each ad has a \emph{budget}: the maximal amount of money
that can be spent on this ad. This model admits a natural variant of UCB1, a
well-known algorithm for multi-armed bandits with stochastic rewards. We derive
strong provable guarantees for this algorithm
Contextual Bandits with Cross-learning
In the classical contextual bandits problem, in each round , a learner
observes some context , chooses some action to perform, and receives
some reward . We consider the variant of this problem where in
addition to receiving the reward , the learner also learns the
values of for all other contexts ; i.e., the rewards that
would have been achieved by performing that action under different contexts.
This variant arises in several strategic settings, such as learning how to bid
in non-truthful repeated auctions (in this setting the context is the decision
maker's private valuation for each auction). We call this problem the
contextual bandits problem with cross-learning. The best algorithms for the
classical contextual bandits problem achieve regret
against all stationary policies, where is the number of contexts, the
number of actions, and the number of rounds. We demonstrate algorithms for
the contextual bandits problem with cross-learning that remove the dependence
on and achieve regret (when contexts are stochastic with
known distribution), (when contexts are stochastic
with unknown distribution), and (when contexts are
adversarial but rewards are stochastic).Comment: 48 pages, 5 figure
Dataretrieving for varied in different Composition Databases using Content aggregation
Keeping in mind with a variety of content choices, consumers are exhibiting diverse preferences for content; their preferences often depend on the context in which they consume content as well as various exogenous events. To satisfy the consumers� demand for such diverse content, multimedia content aggregators (CAs) haveemerged which gather content from numerous multimedia sources. A key challenge for such systems is to accurately predict whattype of content each of its consumers prefers in a certain context,and adapt these predictions to the evolving consumers preferences, contexts, and content characteristics This paper addressesgenerate text based file data sets, such as word, text files, image file data sets, and video file data sets, It also extract data from multiple databases, evaluate user preference based query, reduce time complexity by clustering data, and increase fetching speed by using query classification
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