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Optimizing budget allocation in creator marketing campaigns
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Perustieteiden korkeakoulu |
Bachelor's thesis
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SCI3029
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en
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24
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Budget allocation problem in the Youtube creator marketing context is a sequential resource allocation problem with a budget limitation in an uncertain environment. It can be formulated as a batched and budgeted multi-armed bandit problem. The solution is BB-MAB-TS algorithm for dynamic budget allocation. Thompson Sampling is suggested as the underlying heuristic for sampling the budget allocation weights that are needed to direct the budget allocation optimization system. The result of this thesis is an active learning system that is directed by the BB-MAB-TS algorithm. Multiple future research topics arise from this thesis on the areas of optimizing the convergence time of the optimization process, applying the BB-MAB-TS algorithm to other contexts, and statistical analysis of the performance of the process.
Youtube -vaikuttajamarkkinointikampanjan kontekstissa tapahtuva budjettiallokaatio voidaan kuvailla resurssiallokaatio-ongelmaksi epävarmassa ympäristössä. Budjettiallokaatio tulee optimoida ja se tehdään useiden päätöskierrosten yli itse budjetin rajoittaessa optimointiprosessin pituutta. Kyseinen ongelma muotoillaan batched & budgeted multi-armed bandit -ongelmaksi. Se ratkaistaan dynaamiseen budjettiallokaatioon soveltuvaa työn aikana kehitettyä BB-MAB-TS algoritmia hyödyntäen. Algoritmi valitsee budjettiallokaatiokertoimet perustuen Thompson Samplingia soveltavaan aktiiviseen koneoppimissysteemiin. Täten tämän kandidaatintyön tuloksena on aktiivinen koneoppimissysteemi, joka pohjautuu BB-MAB-TS algoritmiin. Työn seurauksena nousi useita tulevaisuuden tutkimusaiheita kuten oppimisprosessin konvergoitumisajan optimoiminen, BB-MAB-TS -algoritmin soveltaminen muihin resurssiallokaatio konteksteihin, ja oppimisprosessin suoriutumisen tilastollinen analysointi.