AI mune Recommendation Systems Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Kusefa kwekubatana kunobva pachii?
   • Maitiro ekuda kune vakawanda vashandisi
   • Mavara anoshandiswa pamufananidzo wechinhu
   • Hurefu hwekutsanangurwa kwechigadzirwa
   • Kungosandura kabhuku
2. Ko content-based kusefa inoshandisa chii kuita zvinokurudzirwa?
   • Inguva chete yezuva
   • Zvimiro zvezvinhu uye zvaunofarira zvekare
   • Mutengo wemari yekambani
   • coin flip
3. Ndeipi dambudziko iro matrix factorization inogadzirisa mune vanokurudzira?
   • Kuvharidzira mapassword evashandisi
   • Kudzvanya mavhidhiyo mafaera
   • Kushandura sparse mushandisi-chinhu matrix kuita compact latent vectors
   • Routing internet traffic
4. Dambudziko re'kutanga kutonhora' nderei?
   • Maseva ari kutonhora zvakanyanya kuti asashande
   • Utachiona hunomisa maakaundi
   • Mazano anoshanda chete munguva yechando
   • Zvakaoma kurumbidza kune vashandisi vatsva kana zvinhu zvisina nhoroondo
5. Ndeapi makwikwi akabatsira kukurudzira tsvakiridzo yekurudziro?
   • Mubairo weNetflix
   • The Turing Award
   • Mubayiro weNobel
   • Makwikwi eEurovision
6. What did Meyer and colleagues propose the Average Measure of Impact to evaluate?
   • Image classification accuracy
   • The impact of personalized recommendations on usefulness and trust
   • The number of ratings stored in a matrix
   • The GPU memory used by a recommender
7. Which metric is designed to assess the quality of a ranked top-K recommendation list?
   • Mean absolute error on a single rating only
   • Pixel accuracy
   • NDCG@K
   • Kukanganisa kwemazwi
8. Why can a low rating-prediction error fail to guarantee a good recommendation list?
   • RMSE directly measures user satisfaction for every ranking
   • Every user sees every catalog item
   • A rating model cannot use user-item data
   • Rating accuracy and top-item ranking quality are different objectives