# Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting
## 3 Proposed Approach
### Discussion  
The most significant innovation in the proposed approach is its ability to realize item-variance weighting in traditional IBCF. Here, as a key item can play a more significant role in RSs, the recommendations provided to the target user could have satisfactory predictive and classification accuracy.  
The proposed approach employs two techniques to utilize item-variance weighting to improve predictive and classification accuracy in traditional IBCF.  
1. The time-related correlation degree is applied to the similarity computation procedure to improve predictive accuracy.
2. The covering degree is applied to the rating prediction procedure to improve classification accuracy.  
Relative to the first technique, we note that a user's interests may change over time. In traditional IBCF, items rated at different times have the same weight. Thus, some items rated by the user in the past will have the same weight as recently rated items, even if the target user has changed his or her preferences. The time-related correlation degree function can most effectively reduce the weight of items rated by a user over a long period. Thus, we can reduce the effects of changing user preferences over time. After applying the time-related correlation degree to the similarity computation, items recently rated by the same user will have greater weight than items rated in the past. Thus, the similarity can reflect the relationship between items more precisely. Therefore, errors between the predicted and real ratings are reduced and predictive accuracy is improved.  
Relative to the second technique, in traditional IBCF, items in the neighborhood of the target item will have the same weight. In other words, when predicting the rating score for a target item based on rating information from the item's neighborhood, items that are more similar to the target user's preference will have the same weight as items that differ significantly from the target user's preference. Therefore, some items preferred by the target user cannot have higher predicted rating scores than others and it will be difficult to be recommend such items to the target user. The covering degree can measure the relationship between items and the target user's preference. Thus, after applying the covering degree to rating prediction, items that are more similar to the target user's preference will have greater weight than others, which means that these items will have greater contribution when predicting rating scores. As a result, target items with more neighborhoods that are similar to the target user's preference and may be preferred by the user will have relatively higher predicted rating scores. Consequently, it becomes easy to recommend such items to the target user. Therefore, the proposed approach can improve the classification accuracy of recommendations.  
Note that, different from some related approaches which require additional information (e.g., user profiles) that is often not available or incomplete [18; 19; 20; 21; 22; 23], our proposed TCIBCF approach can output recommended items for target user \(tu\) without needing other special information.