Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

AI ni Ile ounjẹ ati Idanwo Iṣeduro Akojọ

Assess food recommendations, interaction filtering, review text, retrieval, embeddings, availability, and user-context limits.

Jẹmọ itọsọna awọn ọnaAI Ni Ile ounjẹ Ati Iṣeduro Akojọ aṣyn
Ibeere 1 ti 8

Kini sisẹ ifowosowopo ni akọkọ da lori lati ṣe awọn iṣeduro?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Kini sisẹ ifowosowopo ni akọkọ da lori lati ṣe awọn iṣeduro?
    • Awọn awoṣe lati ọdọ awọn olumulo pẹlu awọn itọwo ti o jọra
    • Awọn akoonu ijẹẹmu ti satelaiti kọọkan
    • Awọn ounjẹ ká inu ilohunsoke oniru
    • Gidi-akoko idana otutu
  2. Kini idi ti awọn awoṣe NLP iwakusa atunyẹwo ṣe wulo ninu awọn ohun elo ounjẹ?
    • Wọ́n yára sè oúnjẹ náà
    • Wọn ṣe akopọ itara ati jade awọn ifihan agbara ipele satelaiti lati ọrọ
    • Wọn rọpo iwulo fun awọn akojọ aṣayan
    • Wọn ṣeto awọn idiyele ile ounjẹ
  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. Kini awọn ifibọ ti a lo fun ni iṣeduro akojọ aṣayan?
    • Titoju onibara awọn ọrọigbaniwọle
    • Encrypting awọn adirẹsi ifijiṣẹ
    • Wiwọn adiro otutu
    • Ti n ṣojuuṣe awọn awopọ bi awọn olutọpa nitorina awọn ohun kan ti o jọra joko ni isunmọ papọ
  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