Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Jawaab celin toos ah iyo Beddelka Kediska ugu Yar ee labajibbaarada

Fahmo sababta isdhexgalka maqan aysan u ahayn wax aan la jeclayn, sida kalsoonida-miisaanka leh ee ALS ay u beddesho isbeddellada isbeddelka iyo sida soo-gaadhista u saamayso qiimaynta.

Wadooyinka haga ee la xidhiidhaJawaab Celin Daahsoon iyo Beddelka Fagaaraha Ugu Yaraan
Su'aal 1 ee 8

Sidee baa is dhex galka shayga isticmaale ee aan la arkin sida caadiga ah loogu tarjumaa jawaab celin daahsoon?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Sidee baa is dhex galka shayga isticmaale ee aan la arkin sida caadiga ah loogu tarjumaa jawaab celin daahsoon?
    • Sida caddaymaha doorbidida maqan, si toos ah uma aha sida nacaybka
    • Sida la xaqiijiyay qiimeynta taban
    • Iibsi ahaan qiimo eber ah
    • Sida caddaynta isticmaaluhu wuxuu arkay shayga
  2. Muxuu miisaanka kalsoonida c_ui uga dhigan yahay ALS daahsoon?
    • Itimaalka in isticmaaluhu jiro
    • Sida xooggan ee moodalku u aamini karo caddaynta doorbididda lammaanaha
    • Tirada cabbirrada qarsoon
    • Dhibcaha khuseeya shayga la dammaanad qaaday
  3. Maxay ALS u beddeshaa xallinta?
    • Tirada isticmaalayaasha iyo tirada alaabta buugaagta
    • Geed kala soocida iyo qaab kooxeed
    • Qodobbada isticmaalaha oo leh qodobbada shay go'an, ka dibna qodobbada shay ee isticmaalayaashu ay go'an yihiin
    • Qiimaynta cad iyo summada mustaqbalka
  4. Maxay tahay sababta loola dhaqmo dhammaan lamaanaha aan la arkin inay yihiin kuwa aan jeclayn?
    • Gelitaannada maqan had iyo jeer waa eber tiro ahaan macnahooda
    • Qodobbada qarsoon ayaa markaa noqda suurtogalnimada
    • ALS waxay uga baahan tahay isticmaale kasta inuu qiimeeyo shay kasta
    • Walxaha qaar ayaa laga yaabaa in aan weligood loo bandhigin isticmaalaha
  5. Eraygee ujeeddooyinka xaddidaya baaxadda isticmaale iyo qodobbada shay?
    • Set confidence equal to interaction count
    • Penalize overly large user and item factor vectors
    • Guarantee every item is recommended
    • Convert clicks into explicit star ratings
  6. Why use a time-based holdout for recommendation evaluation?
    • It removes popularity bias by definition
    • It guarantees that missing pairs are negatives
    • It tests predictions on interactions that occur after training data
    • It makes all users equally active
  7. Which bias can interaction-only logs create?
    • All event types have the same meaning
    • Every user sees every catalog item
    • Latent factors have no regularization
    • Items not shown cannot generate observed clicks
  8. What should be monitored in addition to ranking accuracy?
    • Catalog coverage and popularity concentration
    • Only the matrix reconstruction objective
    • The alphabetical order of users
    • Whether every latent factor is positive