中文摘要：

在充分分析了传统协同过滤算法推荐精度低,已有的加权改进算法需要额外先验知识和参数优化设置,且只能从某一方面提高推荐精度的基础上,提出相似度最优加权协同过滤推荐模型.该模型以最终推荐评分的平均绝对偏差最小为优化目标,对最近邻的相似度度量进行归一化加权来改进最近邻的选择.该模型在理论上将各个相似性加权算法进行了统一,实现了在没有先验知识下的相似度最优加权.在模型求解的过程中,充分考虑了模型本身的并行性,利用PSO优化算法进行并行参数寻优.在Movie Lens-100k公开数据集上的实验结果表明,相似度最优加权协同过滤推荐模型的评分平均绝对偏差明显小于传统的、相关加权的、IFUBCF和IFIBCF协同过滤算法.

英文摘要：

After comprehensively analyzing the traditional collaborative filtering（ CF） with lowaccuracy and the existing method with weighting which need prior knowledge or optimal parameter settings and can only improve accuracy from a certain aspect,this paper proposes a collaborative filter recommendation model based on the optimal weighted similarity.This model improves the neighbors＇ selection by normalizing similarity of neighbors and uses optimization methods to solve best weight of similarity.This method unifies different weighted algorithm and realizes the best similarity weighted and solution of weight without prior knowledge in theory,besides,it takes into full account its parallelism and searches for the better parameters by the PSO optimization.Experiment results in M ovie Lens-100 K data set shows that M AE of collaborative filter recommendation model based on the optimal weighted similarity is lower than the traditional CF,correlation-weight CF,IFUBCF and IFIBCF.