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Figure 2. Collaborative filtering recommendation system framework based on the user—item interac- tion dataset X composed of u-users and i-items with ratings ranging from 1 to 5, “?” unknown or missing rating. Figure 2. Collaborative filtering recommendation system framework based on the user—item interac-

Figure 2 Collaborative filtering recommendation system framework based on the user—item interac- tion dataset X composed of u-users and i-items with ratings ranging from 1 to 5, “?” unknown or missing rating. Figure 2. Collaborative filtering recommendation system framework based on the user—item interac-

Related Figures (7)

Figure 1. Methodology for developing the AIL. MMRS (DMS: document management software: Logi- calDOC Business version 8.7.3; ML: machine learning, CF: collaborative filtering, NMF: nonnegative matrix factorization) [10,11].
Figure 1. Methodology for developing the AIL. MMRS (DMS: document management software: Logi- calDOC Business version 8.7.3; ML: machine learning, CF: collaborative filtering, NMF: nonnegative matrix factorization) [10,11].
Table 1. Results of the Entropy method: highlighting the five most relevant factors. 2.2. Study’s Database and Input Dataset
Table 1. Results of the Entropy method: highlighting the five most relevant factors. 2.2. Study’s Database and Input Dataset
Table 2. Characteristics and classification of the variables in the input dataset. 2.3. Memory-Based Collaborative Filtering Approach for Mining Methods Selection
Table 2. Characteristics and classification of the variables in the input dataset. 2.3. Memory-Based Collaborative Filtering Approach for Mining Methods Selection
Figure 3. Showing the data pre-processing: transformation of the input dataset for experiments t evaluate the proposed memory-based collaborative filtering approach. Figure 3 illustrates the data pre-processing steps, showing the transformation of the input dataset for experiments to evaluate the proposed memory-based collaborative filtering approach. The transformed input dataset consists of thirty-three mining projects project IDs: PJ001, PJ002, ... PJ033) characterized by the five input variables (OS, RS, TH, SH, and DP) and the corresponding mining methods (SS, RP, SH, Cf, LW, BC and SC) with he respective ratings. In the experiments, the transformed dataset is used to evaluate the effectiveness of the proposed memory-based CF model in predicting and recommending he top-3 most relevant mining methods.
Figure 3. Showing the data pre-processing: transformation of the input dataset for experiments t evaluate the proposed memory-based collaborative filtering approach. Figure 3 illustrates the data pre-processing steps, showing the transformation of the input dataset for experiments to evaluate the proposed memory-based collaborative filtering approach. The transformed input dataset consists of thirty-three mining projects project IDs: PJ001, PJ002, ... PJ033) characterized by the five input variables (OS, RS, TH, SH, and DP) and the corresponding mining methods (SS, RP, SH, Cf, LW, BC and SC) with he respective ratings. In the experiments, the transformed dataset is used to evaluate the effectiveness of the proposed memory-based CF model in predicting and recommending he top-3 most relevant mining methods.
Figure 4. Workflow of the proposed methodology for practical experiments: the memory-based collaborative filtering approach for predicting and recommending top-N mining methods.
Figure 4. Workflow of the proposed methodology for practical experiments: the memory-based collaborative filtering approach for predicting and recommending top-N mining methods.
Figure 5. Performance of the proposed model in predicting primary and top-3 most relevant mining methods in terms of GAR and F1-score. Figure 5 shows the model’s performance results regarding the global accuracy rate (GAR) and the Fl-score. Using the KNN-cosine similarity algorithm to find the nearest projects, it is required to set the parameter k, which is the number of neighbors or projects used for predictions [33]. In the experiments, the model’s accuracy is evaluated for the num- ber of projects (neighborhood size) ranging from 2 to 10. The results show that the model’s performance depends on the number of projects used for predictions (neighborhood size), where the highest accuracy is achieved when using the two nearest projects.
Figure 5. Performance of the proposed model in predicting primary and top-3 most relevant mining methods in terms of GAR and F1-score. Figure 5 shows the model’s performance results regarding the global accuracy rate (GAR) and the Fl-score. Using the KNN-cosine similarity algorithm to find the nearest projects, it is required to set the parameter k, which is the number of neighbors or projects used for predictions [33]. In the experiments, the model’s accuracy is evaluated for the num- ber of projects (neighborhood size) ranging from 2 to 10. The results show that the model’s performance depends on the number of projects used for predictions (neighborhood size), where the highest accuracy is achieved when using the two nearest projects.
Table 3. Performance evaluation results of the classifiers in terms of global accuracy rate (GAR) and average per-class Fl-score. Figure 6. Confusion matrix of the artificial neural network (ANN) model showing the per-class Recall or True Positive Rate (TPR) and the True Negative Rate (TNR). Table 3. Performance evaluation results of the classifiers in terms of global accuracy rate (GAR) and
Table 3. Performance evaluation results of the classifiers in terms of global accuracy rate (GAR) and average per-class Fl-score. Figure 6. Confusion matrix of the artificial neural network (ANN) model showing the per-class Recall or True Positive Rate (TPR) and the True Negative Rate (TNR). Table 3. Performance evaluation results of the classifiers in terms of global accuracy rate (GAR) and
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Mining EngineeringArtificial IntelligenceMachine LearningRecommender SystemsCollaborative FilteringRecommender system, Collaborative filteringMining method selectionRecommender System
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