# An Efficient Approach to Manage Natural Noises in Recommender Systems
## 1 Introduction  
The rapid and deep development of the internet has led to an information overload [1], and it is therefore difficult to obtain valuable information efficiently from vast amounts of data. Recommender systems [2; 3; 4] alleviate the problem and have achieved widespread success in numerous platforms such as Yahoo Music [5], Amazon e-commerce [6], Netflix [7], and YouTube [8]. Collaborative filtering [9; 10; 11], as one of the most popular recommendation techniques, plays a critical role in producing recommendations of high quality. The basic idea of collaborative filtering is to find the neighbors of the target user according to historical ratings and generate predictions by averaging the neighbors' opinions. Therefore, ensuring the accuracy of historic ratings is very important for generating high-quality recommendations. However, some researchers have found that rating information does not always accurately reflect users' preferences, as users are often affected by other factors when rating an item [12; 13; 14]. This means that noises sometimes exist in the ratings, which may mislead the decision of the recommendation algorithm and lower the performance of the recommender system. Generally, the noises in ratings are categorized into malicious noises [15; 16; 17; 18] and natural noises [12; 14; 19; 20; 21]. Malicious noises are user ratings which are deliberately provided by some agents to mislead the recommendation algorithm and distort the recommendation results. For instance, some filmmakers may hire people to give high ratings for their films and low ratings for other films to maximize their business benefits. To address the problems caused by malicious noise, researchers present some techniques to resist attacks of malicious noise [16; 17; 18]. [PERSON] et al. proposed Beta-Protection [16] to alleviate the effect of malicious noises. [PERSON] et al. devised the segmentation approach basedon a dynamic time interval [18], which can detect attacks regardless of the specific attack types. [PERSON] and [PERSON] proposed the Value-Based Neighbor Selection (VNS) approach to detect shilling attacks [17]. Different from malicious noises, natural noises are often ignored in the recommender system. There are two main factors that produce natural noises: one is that preferences of users change over time; the other is that many external factors, such as personal background, social circle, mood, and environment [13], could greatly affect the evaluation of users on an item at some moments. Previous studies have revealed that the natural noises probably distort the results of recommender systems and reduce the accuracy of prediction [14]. To address the problems caused by natural noises, [PERSON] et al. [12] proposed a scheme to detect the ratings caused by natural noise and directly remove them from recommender systems. However, deleting ratings with natural noises makes the available data more sparse, while sparse rating data are not beneficial to generate high-quality recommendation results. [PERSON] et al. [22] corrected the natural noises by asking users to rate items again. [PERSON] and [PERSON] [13] helped users to correct their own ratings by introducing an interactive recommender system. Although these two methods both improve the accuracy of recommendation, they need to collect user rating data multiple times, which increases the workload of data collection. By only utilizing rating information, some studies correct natural noises by re-predicting ratings with collaborative filtering [14; 19; 23; 24; 25]. Although this kind of method does not introduce additional information into the process of correcting natural noise, they need to execute the calculation of prediction twice, which is time-consuming. To avoid this problem, [PERSON] et al. proposed a method that corrects natural noises with a threshold value [20]. To overcome the above problems, we present a new approach to managing natural noises in recommender systems.