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- W4294242010 abstract "We consider the problem of emph{influence maximization}, the problem of maximizing the number of people that become aware of a product by finding the `best' set of `seed' users to expose the product to. Most prior work on this topic assumes that we know the probability of each user influencing each other user, or we have data that lets us estimate these influences. However, this information is typically not initially available or is difficult to obtain. To avoid this assumption, we adopt a combinatorial multi-armed bandit paradigm that estimates the influence probabilities as we sequentially try different seed sets. We establish bounds on the performance of this procedure under the existing edge-level feedback as well as a novel and more realistic node-level feedback. Beyond our theoretical results, we describe a practical implementation and experimentally demonstrate its efficiency and effectiveness on four real datasets." @default.
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- W4294242010 date "2015-02-27" @default.
- W4294242010 modified "2023-09-23" @default.
- W4294242010 title "Influence Maximization with Bandits" @default.
- W4294242010 doi "https://doi.org/10.48550/arxiv.1503.00024" @default.
- W4294242010 hasPublicationYear "2015" @default.
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