Recommendation systems are algorithms for suggesting relevant items to users. Generally, the recommendations are expressed in what will be recommended and a value representing the recommendation's relevance. However, forecasting if the user will buy the recommended item in the next day, week, or month is crucial for companies. The present study describes a process to suggest items from sequential patterns under temporal restrictions. / Tesis
| Identifer | oai:union.ndltd.org:PUCP/oai:tesis.pucp.edu.pe:20.500.12404/18784 |
| Date | 12 April 2021 |
| Creators | Samamé Jimenez, Hilda Ana |
| Contributors | Alatrista Salas, Hugo, Núñez del Prado Cortez, Miguel |
| Publisher | Pontificia Universidad Católica del Perú, PE |
| Source Sets | Pontificia Universidad Católica del Perú |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/closedAccess |
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