Name
Abstract | ||
|---|---|---|
The quality of large-scale recommendation systems has been insufficient in terms of the accuracy of prediction. One of the major reasons is caused by the sparsity of the samples, usually represented by vectors of userspsila ratings on a set of items. Combining information other than userspsila ratings can provide the learning model complementary views of the data and, thus, a more accurate prediction. In this paper, we propose efficient methods for finding the best combination weights among single kernels. The weight parameters are optimized by aligning the combination kernel to ideal kernels. We solve the kernel alignment problem by linear programming techniques. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICMLA.2008.35 | ICMLA |
Keywords | Field | DocType |
learning model,ideal kernel,large-scale recommendation system,single kernel,learning (artificial intelligence),accurate prediction,combining information,combination kernel,information filtering,linear programming,recommendation systems,optimization,major reason,recommendation system,classification,best combination weight,efficient method,data integration,user rating,kernel alignment problem,kernel matrix,kernel alignment problem bylinear,learning artificial intelligence,machine learning,kernel,filtering,recommender system,collaboration,data integrity,accuracy,linear program | Kernel (linear algebra),Data integration,Recommender system,Data mining,Pattern recognition,Computer science,Kernel alignment,Filter (signal processing),Linear programming,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-0-7695-3495-4 | 0 | 0.34 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhonghang Xia | 1 | 120 | 17.28 |
| Houduo Qi | 2 | 437 | 32.91 |
| Manghui Tu | 3 | 123 | 13.25 |
| Wenke Zhang | 4 | 13 | 2.42 |