Name
Abstract | ||
|---|---|---|
In the era of Big Data, users do not express or can't clearly express their wishes and demands. Our work focuses on heterogeneous data extraction and analysis in order to make it easily accessible and exploitable by users or decision-makers. Recently recommendation systems are proved to be efficient in many sectors and newly for human resources to facilitate the recruitment process. For this reason, we present Huntalent, a candidate recommendation system that can represent an interesting solution to optimize the recruitment. This project makes use of Apache Spark, a distributed big data processing framework. Spark gives the advantage of handling iterative and interactive algorithms with efficiency and minimal processing time as compared to traditional map-reduce paradigm. We use content based recommendation techniques to recommend and identify potential candidates from the professional social network LinkedIn. The output of this work can be used by employers to find the right candidate for the right position. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/SNAMS52053.2020.9336532 | 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS) |
Keywords | DocType | ISBN |
Automatic recruitment,Spark,Content based Recommendation System,Human Resource,LinkedIn | Conference | 978-1-6654-1973-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shayma Boukari | 1 | 0 | 0.34 |
| Sondes Fayech | 2 | 0 | 0.34 |
| Rim Faiz | 3 | 98 | 36.23 |