Guide-yakabatana mibvunzo · Pakati Nhanho

AI Recommendation Systems Quiz

Review recommendation methods, collaborative and content filtering, matrix factorization, hybrid systems, and engagement risks.

Nzira dzekutungamira dzinoenderanaAI Recommendation Systems
Mubvunzo 1 ye 8

Ko 'kusefa kwekubatana' kunovimba nei kuita zvinokurudzirwa?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Ko 'kusefa kwekubatana' kunovimba nei kuita zvinokurudzirwa?
    • Mapeteni kune vakawanda vashandisi vane zvinonaka zvakafanana
    • Tsananguro yemavara echinhu chimwe nechimwe chete
    • Mutengo wechigadzirwa
    • A fixed editorial top-10 list
  2. Zvemukati-based kusefa zvinokurudzira zvinhu zvakanyanya ne:
    • Random sampling yekatalogi
    • Kufananidza maficha echinhu kune zvaunofarira zvekare
    • Kuverenga kutengesa kwepasi rose
    • Kukumbira vamwe vashandisi kuti vavhote
  3. What did the Netflix Prize seek to improve by 10% over the Cinematch baseline?
    • Click-through rate for every recommended film
    • The number of films watched per user
    • Root mean squared error (RMSE) on the competition’s held-out ratings
    • A user’s average watch time on the service
  4. Mune matrix factorization, vashandisi uye zvinhu zvinomiririrwa se:
    • Single hongu/kwete mireza
    • Ongororo yemavara akajeka
    • Mazuva ekarenda
    • Vectors ezvakavanzika 'latent factor'
  5. What mechanism did the Deep Interest Network paper propose for modeling a user’s different interests across candidate ads?
    • A local activation unit that forms a candidate-specific interest representation
    • A fixed user vector that is identical for every candidate
    • A rule that selects only the most popular ad
    • A graph algorithm that proves every ad is relevant
  6. A 'hybrid' anokurudzira ndeimwe iyo:
    • Inoshanda pakupera kwevhiki chete
    • Inobatanidza nzira dzekubatana uye dzemukati-based
    • Inoshandisa hapana data zvachose
    • Inongokurudzira zvinhu zvitsva chete
  7. What is one limitation of collaborative filtering for a new item with no interactions?
    • It cannot use ratings
    • It requires item descriptions only
    • It may lack the interaction data needed to recommend the item
    • It always recommends every new item
  8. What should a recommender evaluation measure besides click-through rate?
    • Only the model’s parameter count
    • Only training loss
    • Only the number of recommendations
    • User outcomes and harms such as diversity, exposure, or unwanted engagement