School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, P.R. China.
PLoS One. 2022 Jul 28;17(7):e0271891. doi: 10.1371/journal.pone.0271891. eCollection 2022.
Slope One algorithm and its descendants measure user-score distance and use the statistical score distance between users to predict unknown ratings, as opposed to the typical collaborative filtering algorithm that uses similarity for neighbor selection and prediction. Compared to collaborative filtering systems that select only similar neighbors, algorithms based on user-score distance typically include all possible related users in the process, which needs more computation time and requires more memory. To improve the scalability and accuracy of distance-based recommendation algorithm, we provide a user-item link prediction approach that combines user distance measurement with similarity-based user selection. The algorithm predicts unknown ratings based on the filtered users by calculating user similarity and removing related users with similarity below a threshold, which reduces 26 to 29 percent of neighbors and improves prediction error, ranking, and prediction accuracy overall.
斜率一算法及其衍生算法测量用户评分距离，并使用用户之间的统计评分距离来预测未知评分，而不是典型的协同过滤算法，它使用相似性来选择邻居并进行预测。与仅选择相似邻居的协同过滤系统相比，基于用户评分距离的算法通常在过程中包括所有可能的相关用户，这需要更多的计算时间和内存。为了提高基于距离的推荐算法的可扩展性和准确性，我们提供了一种结合用户距离测量和基于相似性的用户选择的用户-项目链接预测方法。该算法通过计算用户相似度并删除相似度低于阈值的相关用户，根据过滤后的用户预测未知评分，从而减少 26% 到 29%的邻居，并提高整体预测误差、排名和预测准确性。
