IA ci Resto ak Menu Recommandation Quiz

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

1. Lan mooy njëkka aju ci segg lëkkaloo ngir joxe xalaat?
   • Modèle yu bawoo ci jëfandikukat yu nuróo bëgg-bëgg
   • Nutrition bu nekk ci togg bu nekk
   • Design bi ci biir resto bi
   • Tàngooru waañ ci jamono dëgg
2. Lan moo waral modeli PNL yu am njariñ ci aplikaasioŋu lekk yi?
   • Dañu gëna gaaw ci togg
   • Dañuy tënk yëg-yëg bi ba noppi génne siñaal yi ci mbind mi
   • Dañuy wecci bëgg-bëggu menu
   • Dañuy tëral njëgi resto yi
3. In a proposed two-stage food recommender, what would candidate retrieval do before ranking?
   • Confirm the restaurant reservation
   • Process the customer’s payment
   • Select a candidate subset for a later scoring stage
   • Guarantee the final recommendation matches every preference
4. Lan mooy embeddings yi ci digle menu bi?
   • Denc baatu-jàll kiliyaan yi
   • Enkripte adres yi ñuy joxe
   • Natt tàngooru fuur bi
   • Represente plate ni vecteur suko defee mbir yu nuróo toog jegewaale
5. Why can a new diner with no recorded preferences pose a cold-start problem?
   • Interaction-based personalization has little user-history evidence to work from
   • The restaurant must change its menu
   • The model cannot represent food text
   • Opening hours become unavailable to everyone
6. What can the MenuAI research system do with a menu image and a nutritional search key?
   • Book a table without user confirmation
   • Rank menu dishes according to a query such as calories or protein
   • Diagnose a food allergy from a photograph
   • Guarantee a dish is available today
7. Why include restaurant availability or opening hours in recommendations?
   • It improves a model’s parameter count
   • It guarantees the restaurant is highest rated
   • A highly relevant option is not useful if it is unavailable at the requested time
   • It replaces user preferences
8. What is one limitation of review-mining sentiment for food recommendations?
   • Sentiment scores are always objective
   • Reviews contain exact nutrition facts
   • Review mining removes popularity bias
   • A review’s language may be sparse, subjective, or unrepresentative of the current menu