AI muRestaurant uye Menu Recommendation Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Kusefa kwekubatana kunonyanya kutsamira pakuita zvinorudziro?
   • Maitiro kubva kune vashandisi vane zvinonaka zvakafanana
   • Izvo zvinovaka muviri zvendiro imwe neimwe
   • Magadzirirwo emukati meresitorendi
   • Real-time kicheni tembiricha
2. Sei ongororo-migodhi yeNLP modhi inobatsira mumapurogiramu ezvekudya?
   • Vanobika chikafu nekukurumidza
   • Ivo vanopfupikisa manzwiro uye vanobvisa dish-level masaini kubva kune zvinyorwa
   • Vanotsiva kudiwa kwemamenyu
   • Vanoisa mitengo yeresitorendi
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. Chii chinonzi embeddings inoshandiswa mukurudziro yemenu?
   • Kuchengeta mapassword evatengi
   • Kuvharidzira kero dzekutumira
   • Kuyera tembiricha yeovheni
   • Kumiririra ndiro semavekita saka zvinhu zvakafanana zvinogara padhuze
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