# Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting
## 1 Introduction  
Recommender systems (RS) can provide personalized recommendations for various types of users [1; 2; 3; 4]. Thus, such systems are widely used, have significant commercial value and have been studied extensively [5; 6; 7; 8]. RSs rate items that users have not yet purchased and recommend items based on user preferences [9; 10]. Consequently, recommendation accuracy directly affects RS service quality and user experience, because it measures how well an RS can predict an exact rating value for a specific item. Generally, RS accuracy metrics include predictive accuracy and classification accuracy [11; 12]. Predictive accuracy indicates the average error between predictions and real values. For example, in the MovieLens dataset, a user will give each movie rating stars which represent the user's reference degree, the predictive accuracy will evaluate how close predictions are to the user's true number of stars given to each movie. On the other hand, classification accuracy indicates the extent to which the user agrees with the recommendations; it does not attempt to directly measure the ability of an RS to accurately predict ratings [11]. Note that high predictive accuracy of recommendations does notnecessarily imply high classification accuracy, vice versa. It is because even if an RS can correctly rank a user's movie recommendations, it could fail if the predicted rating scores are incorrect. Collaborative filtering (CF) approaches are often used in RSs because they perform well [13; 14; 15]. Item-based collaborative filtering (IBCF) assumes that a user will prefer an item if it is similar to past preferences. Recently, IBCF has demonstrated success in both research and practical applications [16; 17], for example, Amazon. In traditional IBCF, all items are assigned the same weight when computing similarity and predictions; however, it is widely recognized that some items are more important than others and should be given relatively higher weight. It will result in the traditional IBCF often cannot provide recommendations with satisfactory accuracy. Currently, many approaches have been proposed to improve the recommendation accuracy of IBCF; however, their results indicate that they can enhance predictive accuracy or classification accuracy but not in both [18; 19; 20; 21; 22]. Therefore, how to achieve improvements in both predictive and classification accuracy of IBCF is a difficult problem faced by researchers. Motivated by this, in this paper, to provide recommendations with good predictive and classification accuracy at the same time, a new approach named TCIBCF was proposed, which implements the time-aware similarity computation and covering-based rating prediction to realize item-variance weighting in traditional IBCF. Figure 1 indicates the flow chart of our proposed approach. We present a time-related correlation degree and apply it to the item-item similarity computation to improve predictive accuracy, that is, recent user ratings are assigned greater weight. We also propose a covering degree and implement it into rating prediction to increase classification accuracy. Items that are closer to a target user's preference will have a larger covering degree and higher weight. Experimental results demonstrate that the proposed approach outperforms traditional IBCF and other related work and can provide recommendations with satisfactory predictive and classification accuracy. The novel contributions of our approach are featured as follows:  
* Different from most related approaches that applied the time factor into rating prediction step [18; 19; 20], a novel time-related correlation degree function proposed in this paper employs the time factor to compute item-item similarity computation and it can work effectively on the sparse datasets that often occur in real RSs. The predictive accuracy of recommending results can be enhanced effectively by using it and experimental results in Section 4.2 confirm this.