Ringkasan kursus
Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Sekarang Anda sudah tahu cara melakukan hal berikut:
- Jelaskan tujuan sistem rekomendasi.
- Jelaskan komponen sistem rekomendasi, termasuk
pembuatan kandidat, penskoran, dan pemeringkatan ulang.
- Gunakan penyematan untuk merepresentasikan item dan kueri.
- Membedakan antara pemfilteran berbasis konten dan pemfilteran
kolaboratif.
- Jelaskan cara faktorisasi matriks dapat digunakan dalam sistem rekomendasi.
- Jelaskan bagaimana deep neural network dapat mengatasi beberapa batasan
faktorisasi matriks.
- Jelaskan pendekatan pengambilan, penskoran, dan pemeringkatan ulang untuk membangun
sistem rekomendasi.
Kecuali dinyatakan lain, konten di halaman ini dilisensikan berdasarkan Lisensi Creative Commons Attribution 4.0, sedangkan contoh kode dilisensikan berdasarkan Lisensi Apache 2.0. Untuk mengetahui informasi selengkapnya, lihat Kebijakan Situs Google Developers. Java adalah merek dagang terdaftar dari Oracle dan/atau afiliasinya.
Terakhir diperbarui pada 2025-01-13 UTC.
[null,null,["Terakhir diperbarui pada 2025-01-13 UTC."],[[["\u003cp\u003eRecommendation systems predict which items a user will like based on their past behavior and preferences.\u003c/p\u003e\n"],["\u003cp\u003eThese systems use a multi-stage process: identifying potential items (candidate generation), evaluating their relevance (scoring), and refining the order of presentation (re-ranking).\u003c/p\u003e\n"],["\u003cp\u003eEmbeddings play a key role in representing items and user queries, facilitating comparisons for recommendations.\u003c/p\u003e\n"],["\u003cp\u003eTwo primary approaches for recommendation are content-based filtering (using item features) and collaborative filtering (using user similarities).\u003c/p\u003e\n"],["\u003cp\u003eDeep learning techniques enhance traditional methods like matrix factorization, enabling more complex and accurate recommendations.\u003c/p\u003e\n"]]],[],null,["# Course summary\n\n\u003cbr /\u003e\n\nYou should now know how to do the following:\n\n- Describe the purpose of recommendation systems.\n- Explain the components of a recommendation system including candidate generation, scoring, and re-ranking.\n- Use embeddings to represent items and queries.\n- Distinguish between content-based filtering and collaborative filtering.\n- Describe how matrix factorization can be used in recommendation systems.\n- Explain how deep neural networks can overcome some of the limitations of matrix factorization.\n- Describe a retrieval, scoring, re-ranking approach to building a recommendation system."]]
