Research on the Development Strategy of NetEase Cloud Music Based on Decision Tree Algorithm

Dianjin Yang

Dianjin Yang Academy of Music Guizhou Normal University, 550001 Guiyang, Guizhou, China

Abstract

With the rapid development of the internet and digital music, competition among music platforms has become intense. This study focuses on NetEase Cloud Music and proposes a development strategy based on the decision tree algorithm. By collecting user behavior data and constructing decision tree models, the study predicts user preferences and needs, providing personalized recommendations and services on the music platform. Experimental results of this strategy show that it effectively improves user satisfaction and platform profitability. Compared to traditional recommendation algorithms, the decision tree algorithm exhibits better interpretability and accuracy, enabling a deeper understanding of user preferences and behavior patterns, thus providing more precise content recommendations. This innovative development strategy for music platforms helps better meet user demands, achieve sustained growth, and gain competitive advantages.

References

[1] Ratovohery, StÃ©phie., Baudouin, Alexia., Palisson, Juliette., Maillet, Didier., Bailon, Olivier., Belin, Catherine and Narme, Pauline. (2019). Music as a mnemonic strategy to mitigate verbal episodic memory in Alzheimer's disease: Does musical valence matter? Journal of Clinical and Experimental Neuropsychology, 41(10) 10601073. [2] Fritz, B. S., Peklaj, C. (2019). A case study of music instruction according to E. Willemsâ€™ pedagogy in children with intellectual disabilities: Its impacts on music abilities and language skills. International Journal of Music Education, 37 (2) 243â€“256. [3] Pitt, J. (2020). Communicating through musical play: Combining speech and language therapy practices with those of early childhood music educationâ€”the SALT Music approach. Music Education Research, 22 (1) 6886. [4] Canette, L. H., Lalitte, P., Bedoin, N., Pineau, M., Bigand, E., Tillmann, B. (2020). Rhythmic and textural musical sequences differently influence syntax and semantic processing in children. Journal of Experimental Child Psychology, 191, Article 104711. [5] Torpp, R., Huotilainen, M. (2019). Why and how music can be used to rehabilitate and develop speech and language skills in hearing-impaired children. Hearing Research, 380, 108â€“122. [6] Lee, L., Liu, Y.-S. (2021). Use of decision trees to evaluate the impact of a holistic music educational appproach on children with special needs. Sustainability, 13, Article 1410. [7] Merati, N., Siedlikowski, S., Puzhko, S. (2019). In their words: Children's perspectives on an El Sistema music program's effects on their well-being. Progress in Community Health Partnerships: Research, Education, and Action, 13 (4) 359â€“369. [8] Bahzad, Taha Jijo., Adnan, Mohsin Abdulazeez. (2021). Classification based on decision tree algorithm for machine learning. Journal of Advanced Science and Technology, 2 (1) 20â€“28. [9] Zhang, Jiaheng., Fang, Zhiyong., Zhang, Yupeng and Song, Dawn. (2020). Zero knowledge proofs for decision tree predictions and accuracy. Association for Computing Machinery. [10] Matthews, H. (2021). Persuasion, representation, and emotional heightening: The ethical implications of music as a creative practice in the contemporary social documentary. Music, Sound, and the Moving Image, 15 (1) 71â€“93. [11] Park, Keon-Hee., Oh, Sung-Ryul., Son, Hwa-Jeong., Yoo, Jae-Myeong., Kim, S. H and Lee, Guee-Sang. (2008). Decision-tree algorithm for recognition of music score images obtained by mobile phone camera. The Journal of the Korea Contents Association, 8(1). [12] Guggari, Shankru., Kadappa, Vijayakumar V., Umadevi, Abraham, Ajith. (2020). Music rhythm tree-based partitioning approach to decision tree classifier. Journal of King Saud University, 34 (6). [13] Jacquemard, F., Donat-Bouillud, P and Bresson, J. (2015). A structural theory of rhythm notation based on tree representations and term rewriting. In: T. Collins, D. Meredith, & A. Volk (Eds.), Mathematics and Computation in Music (p. 3â€“15). [14] Kotz, S., Ravignani, A.,Fitch, W. (2018). The evolution of rhythm processing. Trends in Cognitive Sciences, 22 (10) 896â€“910. (Special issue: Time in the Brain). [15] Balasubramanian, G., Kanagasabai, A., Mohan, J and Seshadri, N. G. (2018). Music-induced emotion using wavelet packet decomposition. Biomedical Signal Processing and Control, 42, 115â€“128. [16] Aydin-DÃ¼zgit, S. (2019). Popular music and multimodal critical discourse analysis. Journal of Language and Politics, 18 (6) 953â€“955. (Review of Ideology, Control and Resistance in Turkey Since 2002 by Lyndon C. S. Way). [17] Li, C. W., Cheng, T. H, Tsai, C. G. (2019). Music enhances activity in the hypothalamus, brainstem, and anterior cerebellum during script-driven imagery of affective scenes. Neuropsychologia, 133, Article 107073[18] Jamshidi-Naeini, Y., Brown, A. W., Mehta, T (2022). A practical decision tree to support editorial adjudication of submitted parallel cluster randomized controlled trials. Obesity, 30 (3) 565â€“570. [19] Kusudo, K., Ochi, R., Nakajima, S., others. (2022). Decision tree classification of cognitive functions with D2 receptor occupancy and illness severity in late-life schizophrenia. Schizophrenia Research, 241, 113â€“115. [20] Shorabeh, S. N., Samany, N. N., Minaei, F., others. (2022). A decision model based on decision tree and particle swarm optimization algorithms to identify optimal locations for solar power plants construction in Iran. Renewable Energy, 187, 56â€“67. [21] Tsuchida, H., Nishide, T., Maeda, Y. (2022). Private decision tree evaluation with constant rounds via (only) SS-3PC over ring and field. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 105 (3) 214â€“230. [22] Deleenheer, B., Van den Broucke, E., Meulemans, A. (2021). An electronic decision tree to guide physicians in prescribing parenteral nutrition in hospitalised patients: Development, validation and proof of concept.[23] Leo, B. (1996). Heuristics of instability and stabilization in model selection. The Annals of Statistics, 24, 23502383. [24] Leo, B. (1996). Bagging predictors. Machine Learning, 24 (2) 123â€“140. [25] Last, M., Maimon, O., Minkov, E. (2002). Improving the stability of decision trees. International Journal of Pattern Recognition and Artificial Intelligence, 16 (2) 145159. [26] Mirzamomen, Z., Fekri, M. N., Kangavari, M. (2015). Cross split decision trees for pattern classification. In International Conference on Computer and Knowledge Engineering (ICCKE) (p. 240â€“245). [27] Baranauskas, J. A. (2015). The number of classes as a source for instability of decision tree algorithms in high dimensional datasets. Artificial Intelligence Review, 301â€“311. [28] Wang, L., Li, Q., Yu, Y., Liu, J. (2018). Region compatibility-based stability assessment for decision trees. Expert Systems with Applications, 105, 112â€“128.