CAJ | 학술논문

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(P MF )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。
현유사회표첨추천기술존재수거희소、시간복잡도고이급가해석성저등문제,감우차,제출기우개솔구진분해(P MF )진행잠재특정인자연합분해적표첨추천산법(TagRec-UPMF),타결합용호、자원급표첨3방면적잠재특정,연합구건대응적개솔형식적잠재특정향량,연후근거타문량량지간적특정향량내적진행선성조합,종이산생 Top-N 추천。해산법해결료수거규모대차희소정황하적정도문제,산법적선성복잡도사득기가용우대규모수거。실험결과표명,상비우TagRec-CF,PITF,TTD,Tucker,NMF등산법,본문산법기제고료추천적준학솔,우강저료시간손모。여PITF산법상비교,준학솔득도료제고,이처리시간상차불명현；여TTD산법상비교,재준학솔상차불명현적정황하,대대강저료시간손모。인차,본문적TagRec-UPMF산법상비기타산법표현출료일정적우세。
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.