# Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting
## 4 Experiments and Evaluation
### Experimental Results and Comparisons  
We further define TCIBCF to represent the proposed approach using the time-aware similarity computation and covering-based rating prediction, TRIBCF to represent IBCF only using the time-aware similarity computation and CDIBCF to represent IBCF only using the covering-based rating prediction. To demonstrate the performance of the proposed TCIBCF approach and the effect of TCIBCF's different components, we compared our results to TRIBCF approach and CDIBCF approach. In addition, we also compared the proposed approach to the traditional IBCF approach and time-weight item-based collaborative filtering (TWIBCF) proposed by [PERSON] [13]. In all experiments, we used the Pearson correlation coefficient as the similarity measure and the weighted sum was used to predict the rating score. Figures 5 and 6 show the MAE and RMSE results for the MovieLens and Netflix datasets. As can be seen, with increasing neighborhood size, both MAE and RMSE initially decrease then increase with both datasets. With the MovieLens dataset, the MAE and RMSE values between IBCF and CDIBCF are almost the same and the same performance happens in TCIBCF and TRIBCF approaches; however, the MAE and RMSE values of the TCIBCF approach are less than IBCF and TWIBCF approaches. As lower values indicate better predictive accuracy, these results show that time-related correlation can enhance the predictive accuracy, covering degrees have no effect on enhancing the predictive accuracy, so TCIBCF has improved predictive accuracy compared to the traditional IBCF and TWIBCF approaches. With the Netflix dataset, although the values of the evaluation metrics differed, the performance of those approaches was nearly the same as that with the MovieLens dataset. From the above results, we conclude that the time-related correlation can improve predictive accuracy effectively and the predictive accuracy of the proposed TCIBCF approach is better than that of the traditional IBCF and TWIBCF approaches. Figure 7 shows the precision results obtained with the MovieLens and Netflix datasets, respectively. As can be seen, the precision values for those approaches decreased as the neighborhood size increased. Furthermore, the precision values of the IBCF, TRIBCF and TWIBCF approaches were nearly the same and the same performance for TCIBCF and CDIBCF; however, the precision of the TCIBCF and CDIBCF approaches were clearly greater than that of IBCF, TRIBCF and TWIBCF approaches. Figure 5: MAE results with MovieLens dataset (**a**) and Netflix dataset (**b**). Figure 6: RMSE results with MovieLens dataset (**a**) and Netflix dataset (**b**). Figure 8 shows the recall results obtained with the MovieLens and Netflix datasets, respectively. As can be seen, recall increased as the neighborhood size increased with both datasets. TWIBCF and TRIBCF approaches demonstrated nearly the same performance as traditional IBCF, indicating that TWIBCF and TRIBCF cannot improve recall compared to traditional IBCF. However, the recall of TCIBCF and CDIBCF increased faster than the traditional IBCF approach. Moreover, the improvement became larger as the neighborhood size increased. Thus, TCIBCF and CDIBCF can improve the recall of traditional IBCF and outperforms TWIBCF and TRIBCF approaches. Figure 9 shows the F1 results for the MovieLens and Netflix datasets, respectively. As shown, the F1 value increased as the neighborhood size increased with both the MovieLens and Netflix datasets. With the MovieLens dataset, first, all of those approaches showed nearly the same F1 values; however, as the neighborhood size increased, the F1 value of TCIBCF and CDIBCF increased faster than that of the traditional IBCF, TRIBCF and TWIBCF approaches. With the Netflix dataset, the F1 values of TCIBCF and CDIBCF were almost the same; however, the F1 values of TCIBCF and CDIBCF were always greater than that of the other three approaches.