Finding patterns from a user-centric perspective using knowledge discovery methods
[EN] Chained advertisement involves breaking down a marketing campaign message into multiple banners that are shown to a user in a specific sequence in order to create a less intrusive and more effective campaign. The challenge is determining the most effective sequence of websites and banner order....
| Autores: | , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/201765 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/201765 |
| Access Level: | acceso abierto |
| Palabra clave: | User-centric clickstream Sequence Profiling Chained advertisement Recommender systems Probability |
| Sumario: | [EN] Chained advertisement involves breaking down a marketing campaign message into multiple banners that are shown to a user in a specific sequence in order to create a less intrusive and more effective campaign. The challenge is determining the most effective sequence of websites and banner order. This study aims to develop a recommendation system to assist with this issue. To address the vast size of the internet and the complexity of the problem, the research uses a data-driven computational approach to estimate the probability of different sequence events and apply this to real user data from a leading company. The proposed method is faster and more efficient than previous approaches. |
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