Name
Abstract | ||
|---|---|---|
Online Social Network (OSN) is considered a key source of information for real-time decision making. However, several constraints lead to decreasing the amount of information that a researcher can have while increasing the time of social network mining procedures. In this context, this paper proposes a new framework for sampling Online Social Network (OSN). Domain knowledge is used to define tailored strategies that can decrease the budget and time required for mining while increasing the recall. An ontology supports our filtering layer in evaluating the relatedness of nodes. Our approach demonstrates that the same mechanism can be advanced to prompt recommendations to users. Our test cases and experimental results emphasize the importance of the strategy definition step in our social miner and the application of ontologies on the knowledge graph in the domain of recommendation analysis. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.future.2020.09.030 | Future Generation Computer Systems |
Keywords | DocType | Volume |
Social network,Data miner,Big data,Data analysis,Data sampling,Ontology,Recommender system | Journal | 115 |
ISSN | Citations | PageRank |
0167-739X | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamad Arafeh | 1 | 0 | 0.34 |
| Paolo Ceravolo | 2 | 252 | 44.89 |
| Azzam Mourad | 3 | 395 | 38.80 |
| E Damiani | 4 | 164 | 17.12 |
| Emanuele Bellini | 5 | 29 | 12.15 |