湖南大学学报（自然科学版）Issue(10)：107-113,7.
基于PMF进行潜在特征因子分解的标签推荐∗
A Tag Recommending Algorithm with Latent Feature Factor Jointly Factorizing Based on PMF
摘要
Abstract
The existing social tag recommending technology has the problems of data sparsity,high time complexity and low interpretability.To solve these problems,this paper proposed a tag recommen-ding approach called TagRec-UPMF,which j ointly factorizes the latent feature factor based on PMF.The approach jointly builds the corresponding feature vector in the form of probability,combining latent fea-tures of the three different facets of users,resources and tags,and then produces the top-N recommenda-tion according to the linear combination of the inner products between the feature vectors of each pair.The proposed algorithm improves its accuracy in the case of the large size and sparse data,and it can be used for large-scale data due to the linear complexity.Experimental results show that our method has higher ac-curacy and lower time consuming than TagRec-CF,and Tucker,NMF,etc.Meanwhile,the proposed method has better precision than PITF algorithm when their complexity is of little difference.And our method shows lower complexity compared with TTD algorithm while their precision are nearly the same.关键词
协同过滤/潜在特征因子/标签推荐/推荐系统/概率矩阵分解Key words
collaborative filtering/latent feature factor/tag recommender/recommendation system/probabilistic matrix factorization分类
信息技术与安全科学引用本文复制引用
刘胜宗,樊晓平,廖志芳,吴言凤..基于PMF进行潜在特征因子分解的标签推荐∗[J].湖南大学学报（自然科学版）,2015,(10):107-113,7.基金项目
国家科技支撑计划资助项目(2012BAH08B00) （2012BAH08B00）
国家自然科学基金资助项目(61073105),National Natural Science Founda-tion of China(61073105) （61073105）
国家自然科学青年基金资助项目(61202095),Youth Fund of National Natural Science Foundation Projects (61202095) （61202095）
计算机应用技术湖南省“十二五”重点建设学科资助项目 （）
信息技术与信息安全湖南省普通高等学校重点实验室资助项目 （）
