Kuiz Sistem Pengesyoran AI

Jawapan dan penjelasan muncul semasa anda mengambil kuiz.

1. Apakah 'penapisan kolaboratif' bergantung pada untuk membuat pengesyoran?
   • Corak merentas banyak pengguna dengan citarasa yang sama
   • Penerangan teks setiap item sahaja
   • Harga produk
   • Senarai 10 teratas editorial tetap
2. Penapisan berasaskan kandungan mengesyorkan item terutamanya dengan:
   • Persampelan rawak katalog
   • Memadankan ciri item dengan pilihan masa lalu anda
   • Mengira jumlah jualan global
   • Meminta pengguna lain untuk mengundi
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. Dalam pemfaktoran matriks, pengguna dan item diwakili sebagai:
   • Bendera tunggal ya/tidak
   • Ulasan teks biasa
   • Tarikh kalendar
   • Vektor 'faktor terpendam' tersembunyi
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. Pengesyor 'hibrid' ialah yang:
   • Hanya bekerja pada hujung minggu
   • Menggabungkan kaedah kolaboratif dan berasaskan kandungan
   • Tidak menggunakan data sama sekali
   • Mengesyorkan hanya item terbaharu
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