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Data"],"published-print":{"date-parts":[[2019,2,28]]},"abstract":"<jats:p>\n            Influence maximization is the problem of finding influential users, or nodes, in a graph so as to maximize the spread of information. It has many applications in advertising and marketing on social networks. In this article, we study a highly generic version of influence maximization, one of optimizing influence campaigns by sequentially selecting \u201cspread seeds\u201d from a set of\n            <jats:italic>influencers<\/jats:italic>\n            , a small subset of the node population, under the hypothesis that, in a given campaign, previously activated nodes remain persistently active. This problem is in particular relevant for an important form of online marketing, known as\n            <jats:italic>influencer marketing<\/jats:italic>\n            , in which the marketers target a sub-population of influential people, instead of the entire base of potential buyers. Importantly, we make no assumptions on the underlying diffusion model, and we work in a setting where neither a diffusion network nor historical activation data are available. We call this problem\n            <jats:italic>online influencer marketing with persistence<\/jats:italic>\n            (in short, OIMP). We first discuss motivating scenarios and present our general approach. We introduce an estimator on the influencers\u2019\n            <jats:italic>remaining potential<\/jats:italic>\n            \u2013 the expected number of nodes that can still be reached from a given influencer \u2013 and justify its strength to rapidly estimate the desired value, relying on real data gathered from Twitter. We then describe a novel algorithm, GT-UCB, relying on probabilistic upper confidence bounds on the remaining potential. We show that our approach leads to high-quality spreads on both simulated and real datasets. Importantly, it is orders of magnitude faster than state-of-the-art influence maximization methods, making it possible to deal with large-scale online scenarios.\n          <\/jats:p>","DOI":"10.1145\/3274670","type":"journal-article","created":{"date-parts":[[2018,12,20]],"date-time":"2018-12-20T13:35:46Z","timestamp":1545312946000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["Algorithms for Online Influencer Marketing"],"prefix":"10.1145","volume":"13","author":[{"given":"Paul","family":"Lagr\u00e9e","sequence":"first","affiliation":[{"name":"Universit\u00e9 Paris-Sud, Universit\u00e9 Paris-Saclay, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olivier","family":"Capp\u00e9","sequence":"additional","affiliation":[{"name":"CNRS, DI, \u00c9cole normale sup\u00e9rieure, PSL Research University, Inria Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bogdan","family":"Cautis","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Sud, Universit\u00e9 Paris-Saclay, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Silviu","family":"Maniu","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Sud, Universit\u00e9 Paris-Saclay, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,12,19]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"D. 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