AI dalam Kuis Rekomendasi Restoran dan Menu

Jawaban dan penjelasan muncul saat Anda mengikuti kuis.

1. Apa yang terutama diandalkan oleh pemfilteran kolaboratif untuk membuat rekomendasi?
   • Pola dari pengguna dengan selera serupa
   • Kandungan nutrisi pada setiap masakan
   • Desain interior restoran
   • Suhu dapur waktu nyata
2. Mengapa model NLP penambangan ulasan berguna dalam aplikasi makanan?
   • Mereka memasak makanan lebih cepat
   • Mereka merangkum sentimen dan mengekstraksi sinyal tingkat hidangan dari teks
   • Mereka menggantikan kebutuhan akan menu
   • Mereka menetapkan harga restoran
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. Untuk apa penyematan dalam rekomendasi menu?
   • Menyimpan kata sandi pelanggan
   • Mengenkripsi alamat pengiriman
   • Mengukur suhu oven
   • Merepresentasikan piringan sebagai vektor sehingga benda serupa diletakkan berdekatan
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