# Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting
## 4 Experiments and Evaluation
### Discussion  
In the proposed approach, items are assigned different weights in the item-item similarity computation. For example, consider two items \(x\) and \(y\) with the same rating scores from user \(u\) (i.e., \(r_{u,x}=r_{u,y}\)). Here, the time at which the user \(u\) rated target item \(tt\) is \(t_{u,ti}\). In addition, item \(x\) is more recent than the rating time of item \(y\), that is, \(\left|t_{u,ti}-t_{u,x}\right|<\left|t_{u,ti}-t_{u,y}\right|\). In a traditional IBCF, items \(x\) and \(y\) will be given the same similarity as target item \(ti\), because they received the same rating score from the user \(u\). However, the preferences of user \(u\) may have changed over time; thus, the rating of item \(x\) will have greater influence than that of item \(y\). In the proposed approach, \(f(u,ti,x)>f(u,ti,y)\); thus, item \(x\) will obtain higher similarity with the target item than item \(y\) after computing the item-item similarity. In this manner, items that have greater influence on the target item will be selected for the target item's neighborhood. Here, the items selected as the neighborhood of the target item are more reliable; thus, the neighborhood selected by TCIBCF is more similar to the target item than that of traditional IBCF and this reduces error between the predicted and real ratings. Therefore, the proposed TCIBCF approach provided recommendations with better predictive accuracy than traditional IBCF and other related work for both the MovieLens and Netflix datasets.  
After computing the similarity, items that are similar to the target item were selected to predict the rating score. Here, we used the covering degree function to compute the weight of each similar item. For each item \(p\) in the target item's neighborhood, item set \(N_{p}(n)\) captured its characteristics. If \(N_{p}(n)\) has a greater degree of inclusion in the target user's preferences \(R_{tu}\), such that the value of \(CD\Big{(}N_{p}(n),R_{tu}\Big{)}\) is high, this suggests that the characteristics of item \(p\) are nearer to the target user's preferences. If the target item's neighborhood includes many such items, this target item is more likely to be preferred by the target user. For this target item to obtain a higher predicted rating score, item \(p\) will therefore be assigned greater weight than other items when performing rating prediction. Thus, if a target item's neighborhood includes more items with a high covering degree, the target item is more likely to be preferred by the target user and as this item will have a higher predicted rating score,  
Figure 9: F1 results with MovieLens dataset (**a**) and Netflix dataset (**b**).  
it becomes easy to recommend this item to the target user. Therefore, most of items in the target user's recommendation list will be preferred items and the classification accuracy of the proposed approach will be better than that of traditional IBCF and other related work. In summary, recommendations obtained by the proposed approach show accurate predicted rating scores and are preferred by the target user; thus, the proposed approach is more suitable for real-world RS application.