Collaborative Filtering
التصفية التعاونية
استراتيجية توصية تعتمد على سلوك المستخدمين الجمعي: إذا اتفق مستخدمان في تقييماتهما سابقاً فمن المرجح أن يتفقا مستقبلاً. لا تحتاج لمعرفة خصائص العناصر بل فقط أنماط التفاعل.
A recommendation strategy that relies on collective user behavior: if two users agreed in past ratings, they are likely to agree in the future. It requires no item features, only interaction patterns.
Also translated asالترشيح التعاوني، التوصية التعاونية، التصفية بالتعاون
First appears in this corpus in: GroupLens: An Open Architecture for Collaborative Filtering of Netnews (1994)
Appears in these papers
- BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer2019in the sky ✦
- BPR: Bayesian Personalized Ranking from Implicit Feedback2009in the sky ✦
- Deep Interest Network for Click-Through Rate Prediction2018in the sky ✦
- GroupLens: An Open Architecture for Collaborative Filtering of Netnews1994in the sky ✦
- GroupLens: An Open Architecture for Collaborative Filtering of Netnews1994in the sky ✦
- Amazon.com Recommendations: Item-to-Item Collaborative Filtering2003in the sky ✦
- Amazon.com Recommendations: Item-to-Item Collaborative Filtering2003in the sky ✦
- Matrix Factorization Techniques for Recommender Systems2009in the sky ✦
- Matrix Factorization Techniques for Recommender Systems2009in the sky ✦
- Neural Collaborative Filtering2017in the sky ✦
- Neural Collaborative Filtering2017in the sky ✦
- PinSage: Graph Convolutional Neural Networks for Web-Scale Recommender Systems2018in the sky ✦
- Wide & Deep Learning for Recommender Systems2016in the sky ✦
- Deep Neural Networks for YouTube Recommendations2016in the sky ✦
- Deep Neural Networks for YouTube Recommendations2016in the sky ✦