Name
Abstract | ||
|---|---|---|
The amount of scientific conferences and journal articles continues to increase and new approaches are required to support users in finding relevant publications. This study investigates to what extent a new machine learning (ML) pipeline may preferentially identify links between similar scientific articles. The characteristics of intersections and unions of keywords, contextualized keywords (i.e., synsets) and neighbors are computed and used to train a ML model. Automated machine learning (AutoML) is then applied to ease the search for a new pipeline. Extensive experiments demonstrated that a newly designed ML model achieves an accuracy of 90% on a dataset of approximately 120,000 article pairs. These results suggest that application of ML for proposing new recommendation systems could have in the long term a positive impact in the literature. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CEC48606.2020.9185893 | CEC |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hojjat Rakhshani | 1 | 3 | 1.43 |
| Bastien Latard | 2 | 0 | 1.35 |
| Mathieu Brévilliers | 3 | 0 | 0.34 |
| Jonathan Weber | 4 | 92 | 8.97 |
| Julien Lepagnot | 5 | 308 | 19.88 |
| germain forestier | 6 | 467 | 42.14 |
| Michel Hassenforder | 7 | 61 | 11.05 |
| Lhassane Idoumghar | 8 | 145 | 25.07 |