# Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting
## 2 Related Work
### Recommendation Accuracy Problem in Traditional IBCF Approach  
In the traditional IBCF approach, all items have equal weights for both the item-item similarity computation and rating prediction procedures, that is, item weight is not considered. However, it is widely recognized that some items with greater contribution should be assigned relatively higher weight. Therefore, the traditional IBCF often cannot provide recommendations with good predictive and classification accuracy simultaneously. Figure 2 utilizes two items to describe the comparison between predictive accuracy and classification accuracy. If we treat items rated no less than 3 as relevant items, then, in (a) of Figure 2, both item 1 and item 2 have same classification accuracy; however, because error between prediction and real rating of item 1 is bigger than item 2, item 1 will have worse predictive accuracy than item 2. In (b) of Figure 2, the real rating of item 1 is less than 3 but the predicted rating \(p_{u,1}\) is higher than 3, it will result in the classification accuracy of item 1 is worse than item 2. In (c) of Figure 2, both predicted rating and real rating for two items are higher than 3 and errors between them are also the same, so item 1 and item 2 have same results on both predictive accuracy and classification accuracy. From the Figure 2, we can conclude that improving predictive accuracy does not indicate classification accuracy is also improved, vice versa (e.g., (a) and (b) of Figure 2). How to improve predictive and classification accuracy of IBCF simultaneously (e.g., (c) of Figure 2) is a significant problem in the traditional IBCF approach.  
To enhance the accuracy of IBCF, a number of approaches have been explored. According to the performance of these approaches, they can be classified into two lines. In the first line, researchers aim to improve the predictive accuracy of IBCF. [PERSON] et al. [21] incorporated the weighted user-rank into the  
Figure 2: The description of predictive accuracy and classification accuracy using example items. (**a**) with the same classification accuracy, item 1 has worse predictive accuracy than item 2; (**b**) with the same predictive accuracy, item 1 has worse classification accuracy than item 2; (**c**) item 1 and item 2 have same results on both predictive accuracy and classification accuracy.  
computation of item similarities and differentials to propose a PageRank-based ranking approach, this approach can improve the predictive accuracy of IBCF. [PERSON] [22] proposed a niche approach starting from the dynamic preferences of the target user and extracting the influence on user preferences during the entire time period of user modeling; the predictive accuracy of IBCF has been enhanced clearly by this approach. On the other hand, classification accuracy tries to be improved as a target. [PERSON] et al. [18] applied the time weights for different items in the rating prediction step and increased the weights of the influence of ratings relative to time; this approach could improve the classification accuracy of IBCF effectively. [PERSON] et al. [19] developed a temporal overlapping community detection method based on time-weighted association rule mining; this approach can improve classification accuracy by modeling dynamic user interests. However, even approaches mentioned above can enhance predictive accuracy or classification accuracy of IBCF effectively but they cannot enhance them in both.