Name
Title | ||
|---|---|---|
Multi-Dimension Tensor Factorization Collaborative Filtering Recommendation For Academic Profiles |
Abstract | ||
|---|---|---|
The choice of academic itineraries and/or optional subjects to attend is not usually an easy decision since, in most cases, students lack the information, maturity, and knowledge required to make right decisions. This paper evaluates the support of Collaborative Systems for helping and guiding students in this decision-making process, considering the behavior and impact of these systems on the use of data different from the formal information the students usually use. For this purpose, the research applied the clustering based Multi-dimension Tensor Factorization approach to build a recommendation system and confirm that the increment in tensors improves the recommendation accuracy. As a result, this approach permits the user to take advantage of the contextual information to reduce the sparsity issue and increase the recommendation accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-22808-8_21 | ADVANCES IN NEURAL NETWORKS - ISNN 2019, PT II |
Keywords | Field | DocType |
Collaborative filtering, Context aware recommendation system, Contextual Modeling, Item recommendations, Multi-dimensionality, Tensor Factorization | Recommender system,Contextual information,Collaborative filtering,Tensor,Collaboration,Computer science,Artificial intelligence,Tensor factorization,Cluster analysis,Machine learning | Conference |
Volume | ISSN | Citations |
11555 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jesús Silva | 1 | 0 | 1.69 |
| Noel Varela | 2 | 0 | 1.69 |
| Omar Bonerge Pineda Lezama | 3 | 1 | 12.86 |
| Hugo Hernández-P | 4 | 0 | 0.34 |
| Jairo Martínez Ventura | 5 | 0 | 0.34 |
| Boris de la Hoz | 6 | 0 | 0.34 |
| Leidy Pérez Coronel | 7 | 0 | 0.34 |