Academic Journal
Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems
| العنوان: | Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems |
|---|---|
| المؤلفون: | Badrul Sarwar, George Karypis, Joseph Konstan, John Riedl |
| المساهمون: | The Pennsylvania State University CiteSeerX Archives |
| المصدر: | http://www.grouplens.org/papers/pdf/sarwar_SVD.pdf. |
| سنة النشر: | 2002 |
| المجموعة: | CiteSeerX |
| الوصف: | We investigate th use of dimensionality reduction to improve th e performance for a new class of data analysis software called "recommender systems". Recommender systems apply knowledge discovery tech:N4fl4 to th problem of making personalized product recommendations during a live customer interaction. T h tremendous growth of customers and products in recent years poses some key ch]4N]C:5 for recommender systems. Th1F are:producingh igh quality recommendations and performing many recommendations per second for millions of customers and products. Singular Value Decomposition(SVD)-based recommendation algorith0 can quickly produceh igh quality recommendations, buth s to undergo very expensive matrix factorization steps. Inth] paper, we propose and experimentally validate a tech nique th ath as th e potential to incrementally build SVD-based models and promises to maketh recommender systemshm h ly scalable. |
| نوع الوثيقة: | text |
| وصف الملف: | application/pdf |
| اللغة: | English |
| Relation: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.7894; http://www.grouplens.org/papers/pdf/sarwar_SVD.pdf |
| الاتاحة: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.7894 http://www.grouplens.org/papers/pdf/sarwar_SVD.pdf |
| Rights: | Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
| رقم الانضمام: | edsbas.985D6CDB |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |