AI in Restaurant and Menu Recommendation Quiz

Answers and explanations appear as you take the quiz.

1. What does collaborative filtering primarily rely on to make recommendations?
   • Patterns from users with similar tastes
   • The nutritional content of each dish
   • The restaurant's interior design
   • Real-time kitchen temperature
2. Why are review-mining NLP models useful in food apps?
   • They cook the food faster
   • They summarize sentiment and extract dish-level signals from text
   • They replace the need for menus
   • They set restaurant prices
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. What are embeddings used for in menu recommendation?
   • Storing customer passwords
   • Encrypting delivery addresses
   • Measuring oven temperature
   • Representing dishes as vectors so similar items sit close together
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