An Efficient Approach to Manage Natural Noises in Recommender Systems

Recommender systems search the underlying preferences of users according to their historical ratings and recommend a list of items that may be of interest to them. Rating information plays an important role in revealing the true tastes of users. However, previous research indicates that natural noises may exist in the historical ratings and mislead the recommendation results. To deal with natural noises, different methods have been proposed, such as directly removing noises, correcting noise by re-predicting, or using additional information. However, these methods introduce some new problems, such as data sparsity and introducing new sources of noise. To address the problems, we present a new approach to managing natural noises in recommendation systems. Firstly, we provide the detection criteria for natural noises based on the classifications of users and items. After the noises are detected, we correct them with threshold values weighted by probabilities. Experimental results show that the proposed method can effectively correct natural noise and greatly improve the quality of recommendations.

Automated summary

Recommender systems search user preferences based on historical ratings and recommend items of interest. However, natural noises in the ratings can mislead the recommendations. Different methods have been proposed to handle natural noises, but they introduce new problems. To address this, we propose a new approach that detects natural noises based on user and item classifications and corrects them using probability-weighted threshold values. Experimental results show that our method effectively corrects natural noise and improves recommendation quality.