计算机工程Issue(1)：39-44,6.DOI:10.3969/j.issn.1000-3428.2014.01.008
一种基于自适应局部融合参数的协同过滤方法
A Collaborative Filtering Method Based on Adaptive Local Fusion-parameter
摘要
Abstract
Aiming at the problem of data sparsity and dataset heterogeneity in memory-based collaborative filtering recommendation system, this paper proposes a collaborative filtering method based on variable weight similarity computation and Adaptive Local Fusion-parameter(ALFP). The method extracts user emotion information of user-item rating by counting data set to compute user similarity, meanwhile, according to user-item rating quality to improve item similarity computation method. The method then gets ALFP to enhance collaborative filtering’s adaptability to dataset by forecast confidence of user-based method and item-based method. Experimental results show that the method outperforms traditional Global Fusion-parameter(GFP) method by 0.02 with Mean Absolute Error(MAE) in case of data sparsity, it has higher recommendation precision and recommendation coverage, and effectively solves the problem of data sparseness and heterogeneous data sets.关键词
推荐系统/协同过滤/数据稀疏/基于内存的方法/相似度计算/全局融合参数/自适应局部融合参数Key words
recommendation system/collaborative filtering/data sparsity/memory-based method/similarity computation/Global Fusion-parameter(GFP)/Adaptive Local Fusion-parameter(ALFP)分类
信息技术与安全科学引用本文复制引用
程小林,熊焰,刘青文,陆琦玮..一种基于自适应局部融合参数的协同过滤方法[J].计算机工程,2014,(1):39-44,6.基金项目
国家自然科学基金资助项目(61232018,61170233,61272472,61272317,61202404)；博士后基金资助项目(2011M501060) （61232018,61170233,61272472,61272317,61202404）
