Personalized Music Recommendation Algorithm TFPMF

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

Abstract

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)

CLC Number:

TP391

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

References

[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.

[1]	Huang Tao, Zhang Zhi, Ding Yujie, Chen Yanbo, Wang Jing, Zhang Wenqian. Robust Emergency Dispatch Method Considering Dynamic Frequency Security and N-k Contingency [J]. Journal of System Simulation, 2025, 37(12): 2981-2993.
[2]	Zhang Runzhao, Chen Yanbo, Huang Tao, Tian Haoxin, Qiang Tuben, Zhang Zhi. Scheduling Method for Virtual Power Plants Based on Analysis and Forecasting of Heterogeneous Load Characteristics [J]. Journal of System Simulation, 2025, 37(12): 2994-3006.
[3]	Yu Xiangxing, Zhao Yandong, Zhang Baolin. Vibration Control of Offshore Wind Turbine Towers Based on Eddy Current Nonlinear Energy Sink [J]. Journal of System Simulation, 2025, 37(12): 3007-3017.
[4]	Li Bin, Wang Yuchuo. Fault Diagnosis Method for Photovoltaic Systems Based on Multi-strategy Fusion [J]. Journal of System Simulation, 2025, 37(12): 3018-3032.
[5]	Li Xiaobin, Hu Bing, Yin Chao, Li Bo, Ma Jun. Spatiotemporal Graph Convolution-based Demand Forecasting and Simulation Analysis for Automotive Parts Supply Chain [J]. Journal of System Simulation, 2025, 37(12): 3060-3074.
[6]	Peng Yi, Lei Yunkui, Yang Qingqing, Li Hui, Wang Jianming. Improved PID Search Algorithm for UAV Path Planning in Mountainous Environments [J]. Journal of System Simulation, 2025, 37(12): 3075-3086.
[7]	Wu Shuheng, Liu Yongkui, Zhang Lin, Xiao Yingying, Wang Lihui. Lightweight Assembly Workpiece Detection Algorithm Based on Improved YOLOv8 [J]. Journal of System Simulation, 2025, 37(12): 3099-3111.
[8]	Chen Yi, Qiu Sihang, Zhu Zhengqiu, Ji Yatai, Zhao Yong, Ju Rusheng. A Method of Heuristic Human-LLM Collaborative Source Search [J]. Journal of System Simulation, 2025, 37(12): 3112-3127.
[9]	Ren Liang, Zhou Zerong, Ma Yunfeng. Optimization of Order Picking and Sorting Coordintion in “Goods-to-person” System [J]. Journal of System Simulation, 2025, 37(12): 3128-3139.
[10]	Suo Jingyi, Lu Baihong, Qu Che. Measurement of Luminous Intensity Distribution for Film and Television LED Light Sources and Its Simulation Research in Game Engines [J]. Journal of System Simulation, 2025, 37(12): 3140-3151.
[11]	Gong Jianxing, Hu Hai, Ren Haihui, Wu Ruixiang. Interoperability Model and Application of Military Training System for Combination of Virtuality and Reality [J]. Journal of System Simulation, 2025, 37(12): 3161-3175.
[12]	Xu Zhixia, Wang Rui, Sun Nan, He Bing, Shen Xiaowei, Zhu Xiaofei. Research on Cooperative Interference Allocation of Jamming Resources Based on Improved Genetic Algorithm [J]. Journal of System Simulation, 2025, 37(12): 3176-3189.
[13]	Liu Xiang, Jin Qiankun. Research on PAC-Bayes-Based A2C Algorithm for Multi-objective Reinforcement Learning [J]. Journal of System Simulation, 2025, 37(12): 3212-3223.
[14]	Yang Lanying, Li Chao, Zou Haifeng, Wan Jiangtao, Zhang Renqiang, Liu Hui, Lu Hong. Robot Path Planning Optimization Based on Fusion of Improved Ant Colony Algorithm and A* Algorithm [J]. Journal of System Simulation, 2025, 37(11): 2956-2965.
[15]	Su Xiaoting, Zhang Xiaowei, Tian Yi, Li Qi, Wang Shuaihao. Research on Time Sequence Design Method of Dynamic Simulation Scene for Starlight Navigation [J]. Journal of System Simulation, 2025, 37(11): 2946-2955.