Identification of negative keywords in search marketing with embedding layers and neural networks

Autor:	Loris, David
Přispěvatelé:	Vitrià i Marca, Jordi
Rok vydání:	2021
Předmět:	Master's theses Neural networks (Computer science) Xarxes neuronals (Informàtica) Internet advertising Internet searching Master's thesis Treballs de fi de màster Cerca a Internet Publicitat per Internet
Zdroj:	Dipòsit Digital de la UB Universidad de Barcelona
Popis:	Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona. Any: 2021. Tutor: Jordi Vitrià i Marca [en] In this paper we introduce a model to help marketing specialists within the field of Search Advertising to limit spend on Google searches which have a low probability of leading to a revenue generating event. This is a topic which has not been widely addressed in scientific literature. For this study, we obtained data from a company which spends a large amount on Google Ads, but relies on a subjective and time-consuming approach to this problem. Our proposed model uses GloVe’s pre-trained Embedding Layers and Neural Networks to speed up and improve accuracy of this process.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::afaa39942f4dc36614be177ca5667a82 http://hdl.handle.net/2445/183339 Zobrazit plný text záznamu