计算机工程与应用Issue(19)：100-103,134,5.DOI:10.3778/j.issn.1002-8331.1303-0357
融合邻域模型与隐语义模型的推荐算法
Recommender algorithm combined with neighborhood model and LFM
摘要
Abstract
As one of the most successful approaches to building recommender systems, Collaborative Filtering(CF)uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. The two successful approaches to CF are latent factor models, which directly profile both users and products, and neighborhood models, which analyze similarities between products or users. This paper introduces some innovations to both approaches. The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model. Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users. The methods are tested on the Netflix data, and the results are better than those previously published on that dataset.关键词
推荐系统/协同过滤/隐语义模型/均方根误差Key words
recommender systems/Collaborative Filtering(CF)/latent factor model/Root Mean Square Error(RMSE)分类
信息技术与安全科学引用本文复制引用
鲁权,王如龙,张锦,丁怡..融合邻域模型与隐语义模型的推荐算法[J].计算机工程与应用,2013,(19):100-103,134,5.基金项目
国家科技支撑计划项目（No.2012BAF12B20）；国家自然科学基金（No.60901080）。 （）
