quiz bu lëkkale ak tegtal · Diggante Niveau

Quiz ci motëri personaalisasioŋ IA

Jàngaleel ni sistemu recommandé yi di boolee doxalin, man-mani mbir yi, seet kàndidaa yi ak rang yi ngir méngale limu benn jëfandikukat.

Yooni njiit yu jëm ciMotëri Personalisasioŋ IA
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Lan mooy njëkka aju ci segg lëkkaloo?

Xoolaatal laaj bu nekk8 laaj

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

  1. Lan mooy njëkka aju ci segg lëkkaloo?
    • Modèle ci jëfandikukat yu bari, lu melni 'nit ñi bëgg X itam bëgg nañu Y'
    • Tegtal bu nekk ci mbind mi kese
    • Niveau batëri jëfandikukat bi
    • Limu 10 gëna mag yiñ tëral
  2. Luy 'jafe-jafe tàmbali bu sedd' ci personaalisasioŋ?
    • Serveur yi dañuy yàgg lool ngir tàmbali
    • Am ay done yu néew wala amul benn done ci jëfandikukat bu bees wala mbir mu bees
    • Liñu lay digal nga liggéey ci hiver bi kese
    • Jëfandikukat yiy faral di genn
  3. Ci architecture bu am ñaari etap, lan la etap bu njëkk bi (generation kandidaa) di def?
    • Fukki mbir kese lañu ci rang
    • Binndal leeral yi mujj ci produit bi
    • Dafay gaaw wàññi ay milioŋ ciy mbir ba ci ay téemeeri mbir yu néew
    • efaase kontu jëfandikukat yi nekkul
  4. Why might a recommender add a diversity objective to a ranked list?
    • To guarantee every item receives equal clicks
    • To remove all user-history signals
    • To make every recommendation random
    • To avoid filling the list with near-duplicate items and broaden the alternatives shown
  5. Luy 'dugal' ci sistemu xalaat?
    • Vecteur yiy wane jëfandikukat yi ak mbir yi, kon jegewaale bi dafay wane njariñ
    • Puce physique yi ci serwër bi
    • Kuki yiñ denc ci nawigatër bi
    • Mbir yiñ bind ci imeel bi
  6. Lan moo waral feedback bu nëbbu bi (click yi, waxtu seetaan bi) di faral di gëna am solo ci notu star yu leer yi?
    • Naxasaale moo gëna yomb
    • Dafa gëna bari te dafay wane ni nit ñi di doxalee
    • Yoon daf ko sàkku
    • Njortu biddiiw jagul yoon
  7. What does content-based filtering use to recommend items?
    • Only other users’ passwords
    • Random item order
    • Features of items a user has liked or interacted with
    • The device’s battery history
  8. What does normalized discounted cumulative gain (NDCG) evaluate?
    • The number of users who created accounts
    • The model’s training duration
    • Whether an item description is grammatically correct
    • The quality of a ranked list, giving higher weight to relevant items near the top