音乐个性化推荐算法TFPMF的研究

doi:10.16182/j.issn1004731x.joss.17-0256

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (7): 1397-1407.doi: 10.16182/j.issn1004731x.joss.17-0256

音乐个性化推荐算法TFPMF的研究

叶西宁, 王猛

华东理工大学信息科学与工程学院,上海 200237

收稿日期:2017-05-26 修回日期:2017-08-23 发布日期:2019-12-12
作者简介:叶西宁(1968-),女,陕西蒲城,博士,副教授,研究方向为模式识别、数据挖掘、嵌入式系统应用; 王猛(1991-),男,河南沈丘,硕士,研究方向为数据挖掘、模式识别。
基金资助:
国家自然科学基金(61304071)

Personalized Music Recommendation Algorithm TFPMF

Ye Xining, Wang Meng

College of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

Received:2017-05-26 Revised:2017-08-23 Published:2019-12-12

摘要/Abstract

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。

Abstract: 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.

Key words: recommendation system, reciprocal rank, probabilistic matrix factorization, tensor factorization, TFPMF (Tensor Factorization-based Probabilistic Matrix Factorization)

中图分类号:

TP391

叶西宁, 王猛. 音乐个性化推荐算法TFPMF的研究[J]. 系统仿真学报, 2019, 31(7): 1397-1407.

Ye Xining, Wang Meng. Personalized Music Recommendation Algorithm TFPMF[J]. Journal of System Simulation, 2019, 31(7): 1397-1407.

参考文献

[1] Adomavicius G, Tuzhilin A.Context-Aware Recommender Systems[J]. Ai Magazine (S0738-4602), 2010, 16(3): 2175-2178.
[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]// ACM Conference 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 35^th 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 37^th 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.

音乐个性化推荐算法TFPMF的研究

Personalized Music Recommendation Algorithm TFPMF

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

编辑推荐

Metrics

本文评价