Active Learning to Reduce Cold Start in Recommender Systems
Every time a recommender system has a new user, it does not have enough information to generate recommendations with high precision, this is known as cold start. Adapting this problem to a classification problem allow us to apply Active Learning techniques that, as we well see, offer some methods to...
Guardado en:
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2017
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/63482 |
| Aporte de: |
| Sumario: | Every time a recommender system has a new user, it does not have enough information to generate recommendations with high precision, this is known as cold start. Adapting this problem to a classification problem allow us to apply Active Learning techniques that, as we well see, offer some methods to, given the less possible information about a new user, make right predictions with higher precision than the standard solutions applied in this situation. |
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