GEMRec: A Graph-Based Emotion-Aware Music Recommendation Approach

详细信息    查看全文

• 关键词：Music recommendation ; Emotion analysis ; Random walk ; Emotion aware
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：10041
• 期：1
• 页码：92-106
• 全文大小：887 KB
• 参考文献：1.Yuan, B., Xu, B., Chung, T., Shuai, K., Liu, Y.: Mobile phone recommendation based on phone interest. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds.) WISE 2014. LNCS, vol. 8786, pp. 308–323. Springer, Heidelberg (2014). doi:10.​1007/​978-3-319-11749-2_​24
  2.Wu, H., Shao, J., Yin, H., Shen, H.T., Zhou, X.: Geographical constraint and temporal similarity modeling for point-of-interest recommendation. In: Wang, J., Cellary, W., Wang, D., Wang, H., Chen, S.-C., Li, T., Zhang, Y. (eds.) WISE 2015. LNCS, vol. 9419, pp. 426–441. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-26187-4_​40 CrossRef
  3.Kaminskas, M., Ricci, F.: Contextual music information retrieval and recommendation: state of the art and challenges. Comput. Sci. Rev. 6, 89–119 (2012)CrossRef
  4.Wang, K., Zhang, R., Liu, X., Guo, X., Sun, H., Huai, J.: Time-aware travel attraction recommendation. In: Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G. (eds.) WISE 2013. LNCS, vol. 8180, pp. 175–188. Springer, Heidelberg (2013). doi:10.​1007/​978-3-642-41230-1_​15 CrossRef
  5.North, A.C., Hargreaves, D.J.: Situational influences on reported musical preference. Psychomusicology Music Mind Brain 15, 30–45 (1996)
  6.Abowd, G.D., Dey, A.K., Brown, P.J., Davies, N., Smith, M., Steggles, P.: Towards a better understanding of context and context-awareness. In: Handheld and Ubiquitous Computing, pp. 304–307 (1999)
  7.Pettijohn II, T., Williams, G., Carter, T.: Music for the seasons: seasonal music preferences in college students. Curr. Psychol. 29, 328–345 (2010)CrossRef
  8.Kaminskas, M., Ricci, F.: Location-adapted music recommendation using tags. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 183–194. Springer, Heidelberg (2011). doi:10.​1007/​978-3-642-22362-4_​16 CrossRef
  9.Knees, P., Schedl, M.: A survey of music similarity and recommendation from music context data. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 10, 2 (2013)
  10.Wang, X., Rosenblum, D., Wang, Y.: Context-aware mobile music recommendation for daily activities. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 99–108. ACM (2012)
  11.North, A., Hargreaves, D.: The social and applied psychology of music. Oxford University Press (2008)
  12.Chang, C., Lo, C., Wang, C., Chung, P.: A music recommendation system with consideration of personal emotion. In: International Computer Symposium, pp. 18–23 (2010)
  13.Han, B., Rho, S., Jun, S., Hwang, E.: Music emotion classification and context-based music recommendation. Multimedia Tools Appl. 47, 433–460 (2010)CrossRef
  14.Shan, M., Kuo, F., Chiang, M., Lee, S.: Emotion-based music recommendation by affinity discovery from film music. Expert Syst. Appl. 36, 7666–7674 (2009)CrossRef
  15.Deng, S., Wang, D., Li, X., Xu, G.: Exploring user emotion in microblogs for music recommendation. Expert Syst. Appl. 42, 9284–9293 (2015)CrossRef
  16.Ekman, P.E., Davidson, R.J.: The nature of emotion: Fundamental questions. Oxford University Press (1994)
  17.Haveliwala, T.H.: Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search. IEEE Trans. Knowl. Data Eng. 15, 784–796 (2003)CrossRef
  18.Gori, M., Pucci, A.: ItemRank: a random-walk based scoring algorithm for recommender engines. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, pp. 2766–2771. Morgan Kaufmann Publishers Inc., Hyderabad (2007)
  19.Tong, H., Faloutsos, C., Pan, J.-Y.: Fast random walk with restart and its applications. In: Proceedings of the Sixth International Conference on Data Mining, pp. 613–622. IEEE Computer Society (2006)
  20.Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 39–46. ACM, Barcelona (2010)
  21.Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI Press (2009)
  22.Takács, G., Tikk, D.: Alternating least squares for personalized ranking. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 83–90. ACM (2012)
  
• 作者单位：Dongjing Wang (19) (20)
  Shuiguang Deng (19)
  Guandong Xu (20)
  
  19. College of Computer Science and Technology, Zhejiang University, Hangzhou, China
  20. Advanced Analytics Institute, University of Technology Sydney, Sydney, Australia
  
• 丛书名：Web Information Systems Engineering ¨C WISE 2016
• ISBN：978-3-319-48740-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：10041

文摘

Music recommendation has gained substantial attention in recent times. As one of the most important context features, user emotion has great potential to improve recommendations, but this has not yet been sufficiently explored due to the difficulty of emotion acquisition and incorporation. This paper proposes a graph-based emotion-aware music recommendation approach (GEMRec) by simultaneously taking a user’s music listening history and emotion into consideration. The proposed approach models the relations between user, music, and emotion as a three-element tuple (user, music, emotion), upon which an Emotion Aware Graph (EAG) is built, and then a relevance propagation algorithm based on random walk is devised to rank the relevance of music items for recommendation. Evaluation experiments are conducted based on a real dataset collected from a Chinese microblog service in comparison to baselines. The results show that the emotional context from a user’s microblogs contributes to improving the performance of music recommendation in terms of hitrate, precision, recall, and F1 score.