Name
Title | ||
|---|---|---|
Learning Resource Recommendation Based on Generalized Matrix Factorization and Long Short-Term Memory Model |
Abstract | ||
|---|---|---|
Online learning is becoming increasingly popular in recent years. Personalized recommendation is particularly important for the development of online learning systems. Though LSTM model has been widely applied in various recommendations, it normally can't deal with the problem of sparse data. In this paper, we present a novel model for learning resource recommendation, named G-LSTM. Our model integrates the Generalized Matrix Factorization (GMF) with Long Short-Term Memory (LSTM) model. For evaluating our model, we prepare and analyze two datasets from Junyi Academy. Extensive experiments are conducted on the two datasets to verify the superiority of our model in both effectiveness and accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CloudCom.2019.00040 | 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) |
Keywords | Field | DocType |
learning resource recommendation,generalized matrix factorization,long short-term memory,cloud-based learning application | Online learning,Computer science,Matrix decomposition,Long short term memory,Learning resource,Artificial intelligence,Machine learning,Sparse matrix,Distributed computing | Conference |
ISSN | ISBN | Citations |
2330-2194 | 978-1-7281-5012-3 | 0 |
PageRank | References | Authors |
0.34 | 20 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianhang Guo | 1 | 0 | 0.34 |
| Yiping Wen | 2 | 25 | 8.59 |
| Feiran Wang | 3 | 2 | 2.08 |
| Junjie Hou | 4 | 0 | 0.34 |