Alleviating the sparsity problem of collaborative filtering using rough set

Abstract

PurposeThe purpose of this paper is to introduce an improved nearest‐neighbor collaborative filtering algorithm based on rough set theory to alleviate the sparsity problem of collaborative filtering. With experimentations, the new algorithm is thereafter evaluated.Design/methodology/approachNearest‐neighbor algorithm is the earliest proposed and the main collaborative filtering recommendation algorithm, and its recommendation quality is seriously influenced by the sparsity of user ratings. By using rough set theory, the nearest‐neighbor collaborative filtering algorithm can be improved in the sparsity data situation. The union of user rating items is used as the basis of similarity computing among users, and then a rating predicting method based on rough set theory is proposed to estimate missing values in the union of user rating items for decreasing sparsity.FindingsThe sparsity problem of collaborative filtering can be alleviated by using the union of user rating items and estimating missing values based on rough set theory. The experimental results show that the new algorithm can efficiently improve recommendation quality of collaborative filtering.Originality/valueThe union of user rating items was used as the basis of similarity computing among users. A rating prediction method based on rough set theory with an assistant method was proposed to complete the missing values in the union of user rating items. Orthogonal list was used to storage user‐item ratings matrix.