音乐个性化推荐算法TFPMF的研究
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摘要
基于情境感知的个性化推荐是近年来推荐系统中的研究热点和难点问题,数据稀疏是当前推荐系统面临的主要问题。以音乐推荐为背景,改进了多种情境信息的表示方法,将优化排名倒数(RR)的概率矩阵分解模型(RR-PMF)与张量分解相结合,提出了张量概率矩阵分解模型(TFPMF),并使用交叉最小二乘法(ALS)优化该模型。使用last.fm数据集进行仿真实验,通过仿真模型得出TOP-N推荐列表,结果表明该算法在准确率(Precision)、召回率(Recall)和标准化折算累加值(NDCG)评价指标上具有很大的优势,该算法能够有效缓解数据稀疏问题。
In recent years, the personalized context-aware recommendation is the rub and hotness in the research of recommendation system, and the data sparseness is the main problem faced by the current recommendation systems. In the setting of music recommendation, the representing method of varieties of situational information is improved. A model of TFPMF is proposed, which combines the model of RR-PMF with the tensor decomposition. TFPMF is optimized by alternative least squares(ALS). By the simulation experiments in the last.fm dataset, we got the TOP-N recommended list through the simulation program. The simulation results show that the proposed algorithm has great advantages in the evaluation index of Precision, Recall and NDCG, and the algorithm can effectively alleviate the data sparsity problem.
In recent years, the personalized context-aware recommendation is the rub and hotness in the research of recommendation system, and the data sparseness is the main problem faced by the current recommendation systems. In the setting of music recommendation, the representing method of varieties of situational information is improved. A model of TFPMF is proposed, which combines the model of RR-PMF with the tensor decomposition. TFPMF is optimized by alternative least squares(ALS). By the simulation experiments in the last.fm dataset, we got the TOP-N recommended list through the simulation program. The simulation results show that the proposed algorithm has great advantages in the evaluation index of Precision, Recall and NDCG, and the algorithm can effectively alleviate the data sparsity problem.
引文
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[10]王猛,叶西宁.音乐个性化推荐算法RR-UBPMF的研究[J].华东理工大学学报,2017,43(1):113-118.Wang Meng,Ye Xining.RR-UBPMF,A personalized music recommendation algorithm[J].Journal of East China University of Science and Technology(Natural Science Edition),2017,43(1):113-118.
[11]Kolda T G,Bader B W.Tensor decompositions and applications[J].SIAM Review(S0036-1445),2009,51(3):455-500.
[12]Pilászy I,Zibriczky D,Tikk D.Fast als-based matrix factorization for explicit and implicit feedback datasets[C]//Proceedings of the fourth ACM conference on Recommender systems.Barcelona:ACM,2010:71-78.
[2]Baltrunas L,Ricci F.Context-based splitting of item ratings in collaborative filtering[C]//Proceedings of the third ACM conference on Recommender systems.New York:ACM,2009:245-248.
[3]Karatzoglou A,Amatriain X,Baltrunas L,et al.Multiverse recommendation:n-dimensional tensor factorization for context-aware collaborative filtering[C]//Proceedings of the fourth ACM conference on Recommender systems.Barcelona:ACM,2010:79-86.
[4]C Tianqi,Z Weinan,L Qiuxia,et al.SVDFeature:a toolkit for feature-based collaborative filtering[J].Journal of Machine Learning Research(S1532-4435),2012,13(1):3619-3622.
[5]Hidasi B.Factorization models for context-aware recommendations[J].Infocommun J(S0219-1377),2014,VI(4):27-34.
[6]Baltrunas L,Ludwig B,Ricci F.Matrix factorization techniques for context aware recommendation[C]//ACMConference on Recommender Systems,Recsys 2011.USA:Chicago,2011:301-304.
[7]Baltrunas L,Kaminskas M,Ludwig B,et al.InCarMusic:Context-Aware Music Recommendations in a Car[J].Lecture Notes in Business Information Processing(S1865-1356),2011,85:89-100.
[8]Yue S,Karatzoglou A,Baltrunas L,et al.TFMAP:optimizing MAP for top-n context-aware recommendation[C]//Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval.Geneva:ACM,2012:155-164.
[9]Nguyen T V,Karatzoglou A,Baltrunas L.Gaussian process factorization machines for context-aware recommendations[C]//Proceedings of the 37th international ACM SIGIR conference on Research&development in information retrieval.Portland:ACM,2014:63-72.
[10]王猛,叶西宁.音乐个性化推荐算法RR-UBPMF的研究[J].华东理工大学学报,2017,43(1):113-118.Wang Meng,Ye Xining.RR-UBPMF,A personalized music recommendation algorithm[J].Journal of East China University of Science and Technology(Natural Science Edition),2017,43(1):113-118.
[11]Kolda T G,Bader B W.Tensor decompositions and applications[J].SIAM Review(S0036-1445),2009,51(3):455-500.
[12]Pilászy I,Zibriczky D,Tikk D.Fast als-based matrix factorization for explicit and implicit feedback datasets[C]//Proceedings of the fourth ACM conference on Recommender systems.Barcelona:ACM,2010:71-78.