Accuracy of the model
| Article ID | Tests conducted | Implementation | Accuracy of the model | Reliability measure |
|---|---|---|---|---|
| 1 | Training dataset: 70% of data Testing dataset: 30% of data | C4.5 algorithm through WEKA’s J48 | 78.84% were correctly classified and 21.16% were incorrectly classified | Kappa Statistics: 0.7839 Mean Absolute Error: 0.0058 (means the magnitude of errors is almost insignificant) |
| 4 | TF-IDF with cosine similarity as the benchmark | Precision at k = {1,3,5,10} Skill embeddings and WMB, with k = 1 | 95% Generated results vary based on methods and k-values | |
| 5 | Comparison of the system’s efficiency with and without pre-processing the JDs with respect to time taken to generate | Proposed hybrid RS algorithm giving significance to the processing of JDs The relevance of unified database having the same schema | Processing JDs increased the chances of the user of getting matched by 103.44% Increased in match score for raw and processed JDs |
| Article ID | Tests conducted | Implementation | Accuracy of the model | Reliability measure |
|---|---|---|---|---|
| 1 | Training dataset: 70% of data | C4.5 algorithm through WEKA’s J48 | 78.84% were correctly classified and 21.16% were incorrectly classified | Kappa Statistics: 0.7839 |
| 4 | TF-IDF with cosine similarity as the benchmark | Precision at k = {1,3,5,10} | 95% | |
| 5 | Comparison of the system’s efficiency with and without pre-processing the JDs with respect to time taken to generate | Proposed hybrid RS algorithm giving significance to the processing of JDs | Processing JDs increased the chances of the user of getting matched by 103.44% |
Source(s): Tables by author
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