Name
Abstract | ||
|---|---|---|
Recommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in technology enhanced learning such as recommending resources (e.g. papers, books,...) to the learners (students). In this study, we propose using state-of-the-art recommender system techniques for predicting stu- dent performance. We introduce and formulate the problem of predicting student performance in the context of recommender systems. We present the matrix factorization methods, known as the most effective recom- mendation approaches, to implicitly take into account the latent factors, e.g. "slip" and "guess", in predicting student performance. Moreover, the knowledge of the learners has been improved over the time, thus, we propose tensor factorization methods to take the temporal effect into account. Experimental results show that the proposed approaches can improve the prediction results. |
Year | Venue | Keywords |
|---|---|---|
2011 | CSEDU | recommender system,matrix factorization,e commerce |
Field | DocType | Citations |
Recommender system,Computer science,Matrix (mathematics),Matrix decomposition,Theoretical computer science,Artificial intelligence,Tensor factorization,Multimedia,Machine learning | Conference | 16 |
PageRank | References | Authors |
1.29 | 18 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nguyen Thai-Nghe | 1 | 141 | 14.69 |
| Lucas Drumond | 2 | 395 | 24.27 |
| Tomas Horváth | 3 | 61 | 4.79 |
| Alexandros Nanopoulos | 4 | 1856 | 95.35 |
| Lars Schmidt-Thieme | 5 | 3802 | 216.58 |