Prueba de retroalimentación implícita y mínimos cuadrados alternos

Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.

1. ¿Cómo debería interpretarse normalmente una interacción usuario-elemento no observada en la retroalimentación implícita?
   • As missing preference evidence, not automatically as dislike
   • As a confirmed negative rating
   • As a purchase with zero price
   • As proof the user saw the item
2. ¿Qué representa un peso de confianza c_ui en ALS implícita?
   • The probability that a user exists
   • How strongly the model trusts the preference evidence for a pair
   • The number of latent dimensions
   • A guaranteed item relevance score
3. ¿Qué alterna ALS entre resolver?
   • The number of users and the number of catalog items
   • A classification tree and a cluster model
   • User factors with item factors fixed, then item factors with users fixed
   • Explicit ratings and future labels
4. ¿Por qué puede ser engañoso tratar a todos los pares no observados como disgustos?
   • Missing entries are always numerical zeros in meaning
   • The latent factors then become probabilities
   • ALS requires every user to rate every item
   • Some items may never have been exposed to the user
5. ¿Qué término objetivo limita la magnitud de los factores de usuario y artículo?
   • Set confidence equal to interaction count
   • Penalize overly large user and item factor vectors
   • Guarantee every item is recommended
   • Convert clicks into explicit star ratings
6. Why use a time-based holdout for recommendation evaluation?
   • It removes popularity bias by definition
   • It guarantees that missing pairs are negatives
   • It tests predictions on interactions that occur after training data
   • It makes all users equally active
7. Which bias can interaction-only logs create?
   • All event types have the same meaning
   • Every user sees every catalog item
   • Latent factors have no regularization
   • Items not shown cannot generate observed clicks
8. What should be monitored in addition to ranking accuracy?
   • Catalog coverage and popularity concentration
   • Only the matrix reconstruction objective
   • The alphabetical order of users
   • Whether every latent factor is positive