Abstract In this paper, a modified collaborative filtering (MCF) algorithm with improved performance is developed for recommendation systems with application in predicting baseline data of Friedreich’s Ataxia (FRDA) patients. The proposed MCF algorithm combines the individual merits of both the user-based collaborative filtering (UBCF) method and the item-based collaborative filtering (IBCF) method, where both the positively and negatively correlated neighbors are taken into account. The weighting parameters are introduced to quantify the degrees of utilizations of the UBCF and IBCF methods in the rating prediction, and the particle swarm optimization algorithm is applied to optimize the weighting parameters in order to achieve an adequate tradeoff between the positively and negatively correlated neighbors in terms of predicting the rating values. To demonstrate the prediction performance of the proposed MCF algorithm, the developed MCF algorithm is employed to assist with the baseline data collection for the FRDA patients. The effectiveness of the proposed MCF algorithm is confirmed by extensive experiments and, furthermore, it is shown that our algorithm outperforms some conventional approaches.

中文翻译：

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一种基于用户和项目的最佳加权协同过滤方法，用于预测弗里德赖希共济失调患者的基线数据

摘要 在本文中，为推荐系统开发了一种改进的协作过滤（MCF）算法，该算法具有改进的性能，可用于预测弗里德赖希共济失调（FRDA）患者的基线数据。所提出的 MCF 算法结合了基于用户的协同过滤 (UBCF) 方法和基于项目的协同过滤 (IBCF) 方法的各自优点，其中正相关和负相关的邻居都被考虑在内。引入加权参数来量化UBCF和IBCF方法在评分预测中的利用程度，并应用粒子群优化算法优化加权参数，以在正相关和负相关的邻居之间实现充分的权衡。预测评级值的术语。为了证明所提出的 MCF 算法的预测性能，采用开发的 MCF 算法来协助 FRDA 患者的基线数据收集。大量实验证实了所提出的 MCF 算法的有效性，此外，还表明我们的算法优于一些传统方法。