A Slope One Collaborative Filtering Recommendation Algorithm Using Uncertain Neighbors Optimizing

Abstract

Collaborative filtering is one of widely-used techniques in recommendation systems. Data sparsity is a main factor which affects the prediction accuracy of collaborative filtering. Slope One algorithm uses simple linear regression model to solve data sparisity problem. Combined with users’ similarities, k-nearest-neighborhood method can optimize the quality of ratings made by users participating in prediction. Based on Slope One algorithm, a new collaborative filtering algorithm combining uncertain neighbors with Slope One is presented. Firstly, different numbers of neighbors for each user are dynamically selected according to the similarities with other users. Secondly, average deviations between pairs of relevant items are generated on the basis of ratings from neighbor users. At last, the object ratings are predicted by linear regression model. Experiments on the MovieLens dataset show that the proposed algorithm gives better recommendation quality and is more robust to data sparsity than Slope One. It also outperforms some other collaborative filtering algorithms on prediction accuracy.