Optimizing personalized web advertising with machine learning
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Journal Title
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School of Business |
Bachelor's thesis
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Authors
Date
2023
Department
Major/Subject
Mcode
Degree programme
Tieto- ja palvelujohtaminen
Language
en
Pages
22+3
Series
Abstract
As time spent on the internet increases, web advertising plays an increasingly important role. The personalization of web advertisements has achieved good results and advertisers are aware of the advantage of a personalized advertisement banner compared to a generic one. This personalization process requires a huge amount of data about the user's online behavior. The desired data contains information about, for example, the user's interests and purchase history, and it is collected from web servers. Machine learning is a great approach for large amounts of raw data and its core methods, supervised, unsupervised and reinforcement learning, can all be used to get the most out of personalized advertisements. This literature review studies the use of these methods in the different stages of the web advertisement personalization process. As a result, it was found that combining different methods produces the best possible result. Another key finding was that a supervised learning method, deep learning, outperforms traditional machine learning techniques in accuracy. The biggest challenges for optimizing personalization are overpersonalization and privacy concerns.Description
Thesis advisor
Bragge, JohannaKeywords
machine learning, personalization, web advertising, recommendation