## 4 Algorithm Tuning and Experimental Evaluation
### Rating Prediction Accuracy Improvement Achieved by the Proposed Algorithm  
After the proposed algorithm's optimal setting for the _bsf_ factor has been experimentally determined, we present our findings regarding the performance gains in terms of rating prediction accuracy, stemming from the application of the proposed algorithm on the four datasets used in our evaluation (c.f. Table 1). Figure 2 presents the accuracy gains that the proposed algorithm achieves, in terms of the MAE and RMSE metrics, when using the PCC similarity metric and taking the performance of the plain CF algorithm as a yardstick. The proposed algorithm achieves an average prediction MAE reduction of 2.1% and an average prediction RMSE reduction of 2.0%, when using the PCC user similarity metric. Examining each dataset individually, the performance edge of the proposed algorithm against the plain CF algorithm ranges from 1.3% and 1.3% (for the MovieLens 100K dataset) to 2.7% and 2.5% (for the Amazon \"Videogames\" dataset), for the MAE and the RMSE metrics, respectively. Figure 3 presents the accuracy gains achieved by the proposed algorithm in terms of the MAE and RMSE metrics, when using the CS similarity metric and again taking the performance of the plain CF algorithm as a yardstick. Figure 3: MAE and RMSE reduction achieved by the proposed algorithm, when using the CS user similarity metric. Figure 2: MAE and RMSE reduction achieved by the proposed algorithm, when using the PCC user similarity metric. The proposed algorithm achieves an average prediction MAE reduction of 2% and an average prediction RMSE reduction of 2%, as well, when using the CS user similarity metric. At the individual dataset level, the performance edge of the proposed algorithm against the plain CF algorithm, ranges from 1.2% and 1.1% (for the MovieLens 100K dataset) to 2.3% and 2.5% (for the Amazon \"Videogames\" dataset), for the MAE and the RMSE metrics, respectively. Combining the Proposed Algorithm with a Second Algorithm Targeting Rating Prediction Accuracy Improvement  
As stated in the introduction, the proposed algorithm can be easily fused with other CF approaches, aiming to enhance rating prediction accuracy. The rationale behind the evaluation of the combination of the proposed algorithm with another algorithm is that, currently, many recommender systems have been implemented and use diverse algorithms that aim to achieve increased accuracy. A recommender system administrator may wonder whether the algorithm employed in their system needs to be replaced by the proposed one, and which would be the resulting benefits, or whether the proposed algorithm may be combined with the one already employed and if so, what would the benefits be. As a result, the following experiment offers useful insight regarding the additional accuracy gains that may be reaped for existing recommender systems, if the proposed algorithm is incorporated to complement any existing algorithm(s). Towards this direction, the third experiment aims at assessing the rating prediction accuracy improvement when combining the proposed algorithm with another CF rating prediction accuracy approach. In particular, we report on our experiments where the proposed algorithm is combined with the CF\({}_{\text{EPC}}\) algorithm [54]. The CF\({}_{\text{EPC}}\) algorithm is a state-of-the-art algorithm (published towards the end of 2020), also targeting at improving the CF rating prediction accuracy, and not needing any additional information on the items or the users (e.g., user social relationships or item categories). Hence, it can be also applied in all CF datasets. Figure 4 illustrates the improvement in the MAE achieved by the inclusion/combination of the presented algorithm to the CF\({}_{\text{EPC}}\) algorithm, when using the PCC as the similarity metric and again taking the performance of the plain CF algorithm as a yardstick. Figure 4: MAE reduction achieved by the inclusion of proposed algorithm to the CF\({}_{\text{EPC}}\) algorithm, when using the PCC user similarity metric.