Guide-yakabatana mibvunzo · Pakati Nhanho

AI Personalization Engines Quiz

Learn how recommender systems combine behavior, item features, candidate retrieval and ranking to tailor a list for one user.

Nzira dzekutungamira dzinoenderanaAI Personalization Injini
Mubvunzo 1 ye 8

Kusefa kwemubatanidzwa kunonyanya kuvimba nei?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Kusefa kwemubatanidzwa kunonyanya kuvimba nei?
    • Mapeteni kune vakawanda vashandisi, semuenzaniso. 'vanhu vaifarira X vakafarirawo Y'
    • Tsanangudzo yemavara chete yechinhu chimwe nechimwe
    • Chiyero chebhatiri chemushandisi
    • A fixed editorial top-10 list
  2. Chii chinonzi 'kutonhora-kutanga dambudziko' mukusarudzika?
    • Masevha anotora nguva yakareba kuti atange
    • Kuve nediki kana kusava nedata nezve mushandisi mutsva kana chinhu chitsva
    • Mazano anoshanda chete munguva yechando
    • Vashandisi vanobuda kazhinji
  3. Mune yakajairwa-matanho maviri ekuvaka, yekutanga (mumiriri-chizvarwa) nhanho inoitei?
    • Kunyatsoronga zvinhu gumi chete
    • Inonyora tsananguro dzechigadzirwa chekupedzisira
    • Nekukurumidza inodzikisa mamirioni ezvinhu kusvika kumazana mashoma
    • Inodzima maakaundi emushandisi asingashande
  4. Sei sisitimu yekurumbidza ichiwedzera chinangwa chekushandura runyoro rwakarongwa?
    • 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. Chii chinonzi 'embeddings' muhurongwa hwekurudziro?
    • Vectors inomiririra vashandisi uye zvinhu saka kuswedera kunozivisa kukosha
    • Machipisi enyama musevha
    • Makuki akachengetwa mubrowser
    • Email nyaya mitsara
  6. Sei mhinduro isina kujeka (kudzvanya, nguva yekuona) ichiwanzoremerwa pamusoro pezviyero zvenyeredzi zviri pachena?
    • Zviri nyore kuita fake
    • Iyo yakawanda zvikuru uye inoratidza maitiro chaiwo
    • Zvinodiwa nemutemo
    • Zviyero zvenyeredzi hazvisi pamutemo
  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