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,2015,42(10):107~113
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
  
DOI:
中文关键词:  协同过滤  潜在特征因子  标签推荐  推荐系统  概率矩阵分解
英文关键词:collaborative filtering  latent feature factor  tag recommender  recommendation system  probabilistic matrix factorization
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作者单位
刘胜宗,樊晓平,廖志芳,吴言凤 (1.中南大学 信息科学与工程学院,湖南 长沙4100752.中南大学 软件学院,湖南 长沙4100753.湖南财政经济学院 网络化系统研究所,湖南 长沙410205) 
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中文摘要:
      现有社会标签推荐技术存在数据稀疏、时间复杂度高以及可解释性低等问题,鉴于此,提出基于概率矩阵分解(PMF)进行潜在特征因子联合分解的标签推荐算法(TagRec-UPMF),它结合用户、资源及标签3方面的潜在特征,联合构建对应的概率形式的潜在特征向量,然后根据它们两两之间的特征向量内积进行线性组合,从而产生Top-N推荐.该算法解决了数据规模大且稀疏情况下的精度问题,算法的线性复杂度使得其可用于大规模数据.实验结果表明,相比于TagRec-CF,PITF, TTD,Tucker,NMF等算法,本文算法既提高了推荐的准确率,又降低了时间损耗.与PITF算法相比较,准确率得到了提高,而处理时间相差不明显;与TTD算法相比较,在准确率相差不明显的情况下,大大降低了时间损耗.因此,本文的TagRec-UPMF算法相比其他算法表现出了一定的优势.
英文摘要:
      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 recommending approach called TagRec-UPMF, which jointly factorizes the latent feature factor based on PMF. The approach jointly builds the corresponding feature vector in the form of probability, combining latent features of the three different facets of users, resources and tags, and then produces the top-N recommendation 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 accuracy 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.
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