# An Efficient Approach to Manage Natural Noises in Recommender Systems
## 2 Related Work  
In any recommender system, the main types of malicious noises are product push and product nuke attacks [12]. These attacks are generally associated with user profiles. Many methods have been proposed to detect these attacks [16; 17; 18]. The basic idea of these methods is to examine each user's rating behavior to judge whether a particular user profile is a security threat to the recommender system. However, with regards to natural noises, it is difficult to detect them due to their unintentionality, diversity, and quantification [26]. Compared with the case of malicious noises, studies on detecting natural noises are less explored. To detect natural noises, [PERSON] et al. [12] employed the mean absolute error to measure the consistency between the actual ratings and the predicted ratings. If the error is larger than a threshold value, the ratings will be regarded as natural noises. [PERSON] et al. [26] devised another method to detect noisy ratings. Their method is based on the assumption that the ratings from a given user on similar items should have similar values. Based on this assumption, the self-contradiction capturing method is proposed. When the natural noises are found, the noisy rating is removed directly. In this way, the data sparsity problem is made more serious, and the performance of recommender systems may be affected. To avoid the data sparsity problem, some researchers aim to correct the natural noises rather than remove them directly. [PERSON] et al. [22] modified noisy ratings by re-rating (i.e., requiring users to rate the items again). [PERSON] and [PERSON] [13] corrected the natural noises by introducing an interactive recommender system. They focus on the phenomenon that users may commit some mistakes when rating items. Once the user make a mistake, the interactive recommender system will remind them to correct the ratings. However, the cost of requiring users to rate items again or creating an interactive recommender system is very high. Moreover, the methods in [13; 22] may introduce additional information into the recommender system. These additional information themselves contain uncertainty and do not correct natural noises completely. Different from the previous work, [PERSON] et al. [14] modified natural noises by predicting a new value using collaborative filtering. If the difference between the noisy rating and its new rating is larger than a threshold value, the noisy rating will be replaced by the new rating. So, this scheme improves the recommendation quality. [PERSON] et al. [23] incorporated a fuzzy model with re-predicting to correct natural noises. [PERSON] et al. applied the re-predicting technique to group recommender systems [19; 25]. [PERSON] et al. introduced the re-predicting method into multi-criteria recommender systems [24]. Although these re-predicting methods achieve some success in managing noise, they are still time-consuming. To achieve high efficiency, [PERSON] et al. [20] adopted a threshold value to correct natural noises, which is a promising method for managing natural noises. Table 1 summarizes the main methods for dealing with natural noises. To overcome the drawbacks of the existing methods, we present an alternative approach to managing natural noises in recommender systems. Different from the existing methods, we group users into three subcategories: Negative, Average, and Positive. Similarly, items are divided into No-Preferred, Av-Preferred, and Preferred. Then, we consider the probability that a user belongs to each subcategory and correct the natural noise with threshold