隨著網路以及行動裝置的快速發展，使用者接收到的資訊量也隨之增加，推薦系統也因此被大量的使用以幫助使用者快速找到需要的資訊。本篇論文著重在群體音樂推薦系統，相較於個人音樂推薦系統，群體音樂推薦系統推薦的內容更需要在使用者不同的意見中找到共識，我們的演算法透過分析使用者的收聽行為找出使用者有興趣的歌曲並結合歌曲的熱門程度建立多重滿意度指標優化之模型，提高推薦的準確度以及使用者整體的滿意度。適度的推薦熱門歌曲可以提高準確度但若過度推薦熱門的歌曲會讓使用者覺得沒有驚喜因而降低使用推薦系統的意願。為了解決這個問題，我們提出的演算法透過 Collaborative Filtering 的方法預測出使用者的喜好並藉由學習使用者對於熱門歌曲的接受程度來優化推薦的清單。分析結果顯示我們提出的推薦系統不僅在準確度有明顯的提升，也確保推薦內容的多樣性以及新穎性，藉由多重指標優化來提高使用者的整體滿意度。

Recommendation system is designed to provide recommendation lists to prevent information overload. This paper focuses on music group recommendation. We first discover users' preference and track popularity from their listening history. The popularity of the tracks can be represented as the possibility how much a user might like a track. Considering track popularity and group popularity tendency helps to improve the accuracy of the recommendation. However, recommending too many popular tracks may decrease the diversity and novelty of the recommendation lists, and further influences the satisfaction of the users. Thus, we try to utilize latent factor models along with track popularity to optimize the performance of the recommendation algorithm. The proposed framework intelligently learns the popularity tendency of the group members and leveraging track popularity and the quality of recommendation. Here, we think good quality of recommendation not only means high accuracy but also great diversity and novelty. Evaluation results show that the proposed methods give recommendation with great quality in aspect of accuracy, diversity, and novelty.

[1] (2015) IFPI global music report 2015. IFPI. [Online]. Available: https://www.riaa.com/wp-content/uploads/2015/09/Digital-Music-Report-2015.pdf
[2] G. Fazekas, M. Barthet, and M. B. Sandler, “Demo paper: The bbc desktop jukebox music recommendation system: A large scale trial with professional users,” in 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), 2013, pp. 1–2.
[3] H. Han, X. Luo, T. Yang, and Y. Shi, “Music recommendation based on feature similarity,” in 2018 IEEE International Conference of Safety Produce Informatization (IICSPI), 2018, pp. 650–654.
[4] D. Wu, “Music personalized recommendation system based on hybrid filtration,”in 2019 International Conference on Intelligent Transportation, Big Data Smart City (ICITBS), 2019, pp. 430–433.
[5] E. Shakirova, “Collaborative filtering for music recommender system,” in 2017
IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 2017, pp. 548–550.
[6] S. Dara, C. R. Chowdary, and C. Kumar, “A survey on group recommender systems,” Journal of Intelligent Information Systems, vol. 54, no. 2, pp. 271–295, Apr 2020. [Online]. Available: https://doi.org/10.1007/s10844-018-0542-3
[7] J. F. McCarthy and T. D. Anagnost, “Musicfx: an arbiter of group preferences for computer supported collaborative workouts,” in Proceedings of the 1998 ACM conference on Computer supported cooperative work, 1998, pp. 363–372.
[8] A. Crossen, J. Budzik, and K. J. Hammond, “Flytrap: intelligent group music recommendation,” in Proceedings of the 7th international conference on Intelligent user interfaces, 2002, pp. 184–185.
[9] D. Qin, X. Zhou, L. Chen, G. Huang, and Y. Zhang, “Dynamic connection-based social group recommendation,” IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 3, pp. 453–467, 2020.
[10] S. B. Abdrabbah, R. Ayachi, and N. B. Amor, “Collaborative filtering based on dynamic community detection,” Dynamic Networks and Knowledge Discovery, vol. 85, 2014.
[11] J. Castro, J. Lu, G. Zhang, Y. Dong, and L. Martínez, “Opinion dynamics-based group recommender systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 12, pp. 2394–2406, 2018.
[12] D. Sacharidis, “Top-n group recommendations with fairness,” in Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, ser. SAC ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 1663–1670. [Online]. Available: https://doi.org/10.1145/3297280.3297442
[13] M. Schedl, “Deep learning in music recommendation systems,” Frontiers in Applied Mathematics and Statistics, vol. 5, p. 44, 2019.
[14] Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, vol. 42, no. 8, pp. 30–37, 2009.
[15] Y. Song, S. Dixon, and M. Pearce, “A survey of music recommendation systems and future perspectives,” in 9th International Symposium on Computer Music Modeling and Retrieval, vol. 4, 2012, pp. 395–410.
[16] K. Niu, X. Zhao, F. Li, N. Li, X. Peng, and W. Chen, “Utsp: User-based two-step recommendation with popularity normalization towards diversity and novelty,” IEEE Access, vol. 7, pp. 145 426–145 434, 2019.
[17] S. Vargas and P. Castells, “Improving sales diversity by recommending users to items,” in Proceedings of the 8th ACM Conference on Recommender Systems, ser. RecSys ’14. New York, NY, USA: Association for Computing Machinery, 2014,
p. 145–152. [Online]. Available: https://doi.org/10.1145/2645710.2645744
[18] P. Adamopoulos and A. Tuzhilin, “On over-specialization and concentration bias of recommendations: Probabilistic neighborhood selection in collaborative filtering systems,” in Proceedings of the 8th ACM Conference on Recommender Systems, ser. RecSys ’14. New York, NY, USA: Association for Computing Machinery, 2014, p. 153–160. [Online]. Available: https://doi.org/10.1145/2645710.2645752
[19] M. D. Ekstrand, F. M. Harper, M. C. Willemsen, and J. A. Konstan, “User perception of differences in recommender algorithms,” in Proceedings of the 8th ACM Conference on Recommender Systems, ser. RecSys ’14. New York, NY, USA: Association for Computing Machinery, 2014, p. 161–168. [Online]. Available: https://doi.org/10.1145/2645710.2645737
[20] F. Garcin, B. Faltings, O. Donatsch, A. Alazzawi, C. Bruttin, and A. Huber, “Offline and online evaluation of news recommender systems at swissinfo.ch,” in Proceedings of the 8th ACM Conference on Recommender Systems, ser. RecSys ’14. New York, NY, USA: Association for Computing Machinery, 2014, p. 169–176. [Online]. Available: https://doi.org/10.1145/2645710.2645745
[21] T. Bertin-Mahieux, D. P. Ellis, B. Whitman, and P. Lamere, “The million song dataset,” in Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR 2011), 2011.
[22] I. A. Christensen and S. Schiaffino, “Entertainment recommender systems for group of users,” Expert Systems with Applications, vol. 38, no. 11, pp. 14 127 – 14 135, 2011. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0957417411007482
[23] S. Ghazarian and M. A. Nematbakhsh, “Enhancing memory-based collaborative filtering for group recommender systems,” Expert Systems with Applications, vol. 42, no. 7, pp. 3801 – 3812, 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0957417414007386
[24] L. M. De Campos, J. M. Fernández-Luna, J. F. Huete, and M. A. Rueda-Morales, “Combining content-based and collaborative recommendations: A hybrid approach based on bayesian networks,” International journal of approximate reasoning, vol. 51, no. 7, pp. 785–799, 2010.
[25] A. B. Barragáns-Martínez, E. Costa-Montenegro, J. C. Burguillo, M. Rey-López, F. A. Mikic-Fonte, and A. Peleteiro, “A hybrid content-based and item-based collaborative filtering approach to recommend tv programs enhanced with singular value decomposition,” Inf. Sci., vol. 180, no. 22, p. 4290–4311, Nov. 2010. [Online]. Available: https://doi.org/10.1016/j.ins.2010.07.024
[26] Q. Li and B. M. Kim, “An approach for combining content-based and collaborative filters,” in Proceedings of the Sixth International Workshop on Information Retrieval with Asian Languages - Volume 11, ser. AsianIR ’03. USA: Association for Computational Linguistics, 2003, p. 17–24. [Online]. Available: https://doi.org/10.3115/1118935.1118938
[27] X. Wang and Y. Wang, “Improving content-based and hybrid music recommendation using deep learning,” in Proceedings of the 22nd ACM International Conference on Multimedia, ser. MM ’14. New York, NY, USA: Association for Computing Machinery, 2014, p. 627–636. [Online]. Available: https://doi.org/10.1145/2647868.2654940
[28] Y. Ma, S. Ji, Y. Liang, J. Zhao, and Y. Cui, “A hybrid recommendation list aggregation algorithm for group recommendation,” in 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, 2015, pp. 405–408.
[29] T. Hornung, C. Ziegler, S. Franz, M. Przyjaciel-Zablocki, A. Schätzle, and G. Lausen, “Evaluating hybrid music recommender systems,” in 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 1, 2013, pp. 57–64.
[30] J. Oh, S. Park, H. Yu, M. Song, and S.-T. Park, “Novel recommendation based on personal popularity tendency,” in 2011 IEEE 11th International Conference on Data Mining, 2011, pp. 507–516.
[31] K. Kapoor, V. Kumar, L. Terveen, J. A. Konstan, and P. Schrater, “"i like to explore sometimes": Adapting to dynamic user novelty preferences,” in Proceedings of the 9th ACM Conference on Recommender Systems, ser. RecSys ’15. New York, NY, USA: Association for Computing Machinery, 2015, p. 19–26. [Online]. Available: https://doi.org/10.1145/2792838.2800172
[32] X. Tan, Y. Guo, Y. Chen, and W. Zhu, “Characterizing user popularity reference in a large-scale online video streaming system,” in 6th International Conference on Wireless, Mobile and Multi-Media (ICWMMN 2015), 2015, pp. 246–249.
[33] Y. Hu, Y. Koren, and C. Volinsky, “Collaborative filtering for implicit feedback datasets,” in 2008 Eighth IEEE International Conference on Data Mining, 2008, pp. 263–272.
[34] N. Kim and J. Lee, “Group recommendation system: Focusing on home group user in tv domain,” in 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS), 2014, pp. 985–988.
[35] M. Pacula, “A matrix factorization algorithm for music recommendation using implicit user feedback,” 2009.
[36] A. N. Mikhail Trofimov, “tffm: Tensorflow implementation of an arbitrary order factorization machine,” https://github.com/geffy/tffm, 2016.
[37] S. Rendle, “Factorization machines with libfm,” ACM Trans. Intell. Syst. Technol., vol. 3, no. 3, May 2012. [Online]. Available: https://doi.org/10.1145/2168752.2168771
[38] S. Rendle, “Factorization machines,” in 2010 IEEE International Conference on Data Mining, 2010, pp. 995–1000.
[39] H. Ma, I. King, and M. R. Lyu, “Effective missing data prediction for collaborative filtering,” in Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ser. SIGIR ’07. New York, NY, USA: Association for Computing Machinery, 2007, p. 39–46. [Online]. Available: https://doi.org/10.1145/1277741.1277751