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Recommending More Suitable Music Based on Users’ Real Context

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2018)

Abstract

Music recommendation is an popular function for personalized services and smart applications since it focuses on discovering users’ leisure preference. The traditional music recommendation strategy captured users’ music preference by analyzing their historical behaviors to conduct personalized recommendation. However, users’ current states, such as in busy working or in a leisure travel, etc., have an important influence on their music enjoyment. Usually, those existing methods only focus on pushing their favorite music to users, which may be not the most suitable for current scenarios. Users’ current states should be taken into account to make more perfect music recommendation. Considering the above problem, this paper proposes a music recommendation method by considering both users’ current states and their historical behaviors. First, a feature selection process based on ReliefF method is applied to discover the optimal features for the following recommendation. Second, we construct different feature groups according to the feature weights and introduce Naive Bayes model and Adaboost algorithm to train these feature groups, which will output a base classifier for each feature group. Finally, a majority voting strategy decides the optimal music type and each user will be recommended more suitable music based on their current context. The experiments on the real datasets show the effectiveness of the proposed method.

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References

  1. Baccigalupo, C., Plaza, E.: Case-based sequential ordering of songs for playlist recommendation. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS, vol. 4106, pp. 286–300. Springer, Heidelberg (2006). https://doi.org/10.1007/11805816_22

    Chapter  Google Scholar 

  2. Basu, C., Hirsh, H., Cohen, W.: Recommendation as classification: using social and content-based information in recommendation. In: Fifteenth National/Tenth Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence, pp. 714–720 (1998)

    Google Scholar 

  3. Boratto, L., Carta, S., Fenu, G., Mulas, F., Pilloni, P.: Influence of rating prediction on group recommendation’s accuracy. IEEE Intell. Syst. 31(6), 22–27 (2016)

    Article  Google Scholar 

  4. Chen, T.-Y., Chen, L.-C., Chen, Y.-M.: Mining location-based service data for feature construction in retail store recommendation. In: Perner, P. (ed.) Advances in Data Mining. Applications and Theoretical Aspects. LNCS, vol. 10357, pp. 68–77. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62701-4_6

    Chapter  Google Scholar 

  5. Dai, J., Yang, B., Guo, C., Ding, Z.: Personalized route recommendation using big trajectory data. In: IEEE International Conference on Data Engineering, pp. 543–554 (2015)

    Google Scholar 

  6. Donaldson, J.: A hybrid social-acoustic recommendation system for popular music. In: ACM Conference on Recommender Systems, pp. 187–190 (2007)

    Google Scholar 

  7. Gao, L., Wu, J., Qiao, Z., Zhou, C., Yang, H., Hu, Y.: Collaborative social group influence for event recommendation, pp. 1941–1944 (2016)

    Google Scholar 

  8. Gu, Y., Song, J., Liu, W., Zou, L., Yao, Y.: Context aware matrix factorization for event recommendation in event-based social networks. In: ACM International Conference on Web Intelligence, pp. 248–255 (2017)

    Google Scholar 

  9. Huo, H., Liu, X., Zheng, D., Wu, Z., Yu, S., Liu, L.: Collaborative filtering fusing label features based on SDAE. In: Perner, P. (ed.) Advances in Data Mining. Applications and Theoretical Aspects. LNCS, vol. 10357, pp. 223–236. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62701-4_17

    Chapter  Google Scholar 

  10. Kim, H.H.: A semantically enhanced tag-based music recommendation using emotion ontology. In: Selamat, A., Nguyen, N.T., Haron, H. (eds.) ACIIDS 2013. LNCS, vol. 7803, pp. 119–128. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36543-0_13

    Chapter  Google Scholar 

  11. Kuo, F.F., Shan, M.K.: A personalized music filtering system based on melody style classification. In: IEEE International Conference on Data Mining, p. 649 (2002)

    Google Scholar 

  12. Mathew, P., Kuriakose, B., Hegde, V.: Book Recommendation System through content based and collaborative filtering method. In: IEEE International Conference on Data Mining and Advanced Computing, pp. 47–52 (2016)

    Google Scholar 

  13. Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Eighteenth National Conference on Artificial Intelligence, pp. 187–192 (2002)

    Google Scholar 

  14. Pampalk, E., Pohle, T., Widmer, G.: Dynamic playlist generation based on skipping behavior. In: Proceedings of the International Conference on Music Information Retrieval, ISMIR 2005, London, UK, 11–15 September 2005, pp. 634–637 (2005)

    Google Scholar 

  15. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: International Conference on World Wide Web, pp. 285–295 (2001)

    Google Scholar 

  16. Serbos, D., Qi, S., Mamoulis, N., Pitoura, E., Tsaparas, P.: Fairness in package-to-group recommendations. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017, pp. 371–379. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland (2017). https://doi.org/10.1145/3038912.3052612

  17. Stratigi, M., Kondylakis, H., Stefanidis, K.: Fairness in group recommendations in the health domain. In: IEEE International Conference on Data Engineering (2017)

    Google Scholar 

  18. Tiemann, M., Pauws, S.: Towards ensemble learning for hybrid music recommendation. In: ACM Conference on Recommender Systems, pp. 177–178 (2007)

    Google Scholar 

  19. Wang, X., Dou, L.: Social recommendation algorithm based on the context of time and tags. In: Third International Conference on Advanced Cloud and Big Data, pp. 15–19 (2016)

    Google Scholar 

  20. Wu, W., et al.: Improving performance of tensor-based context-aware recommenders using Bias Tensor Factorization with context feature auto-encoding. Knowl.-Based Syst. 128(C), 71–77 (2017)

    Article  Google Scholar 

  21. Xing, Z., Parandehgheibi, M., Xiao, F., Kulkarni, N., Pouliot, C.: Content-based recommendation for podcast audio-items using natural language processing techniques. In: IEEE International Conference on Big Data, pp. 2378–2383 (2017)

    Google Scholar 

  22. Yang, Q., Yao, X., Jingwei, Z., Zhongqin, B.: Exploiting SDAE model for recommendations. In: the 30th International Conference on Software Engineering & Knowledge Engineering, pp. 11–16 (2018)

    Google Scholar 

  23. Yin, H., Sun, Y., Cui, B., Hu, Z., Chen, L.: LCARS: a location-content-aware recommender system. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 221–229 (2013)

    Google Scholar 

  24. Yoshii, K., Goto, M., Komatani, K., Ogata, T., Okuno, H.G.: Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences. In: International Conference on Music Information Retrieval, pp. 296–301 (2006)

    Google Scholar 

  25. Yu, Z., Tian, M., Wang, Z., Guo, B., Mei, T.: Shop-type recommendation leveraging the data from social media and location-based services. ACM Trans. Knowl. Discov. Data 11(1), 1 (2016)

    Article  Google Scholar 

  26. Zhang, J., Yang, C., Yang, Q., Lin, Y., Zhang, Y.: HGeoHashBase: an optimized storage model of spatial objects for location-based services. Front. Comput. Sci. 1–11 (2018)

    Google Scholar 

  27. Zhang, L., Zhou, R., Jiang, H., Wang, H., Zhang, Y.: Item group recommendation: a method based on game theory. In: International Conference on World Wide Web Companion, pp. 1405–1411 (2017)

    Google Scholar 

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Acknowledgments

This study is funded by the National Natural Science Foundation of China (No. 61462017, 61862013, U1501252, U1711263, 61662015), Guangxi Natural Science Foundation of China (No. 2017GXNSFAA198035), and Guangxi Cooperative Innovation Center of Cloud Computing and Big Data.

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Authors and Affiliations

  1. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, 541004, China

    Qing Yang, Le Zhan & Li Han

  2. Guangxi Cooperative Innovation Center of Cloud Computing and Big Data, Guilin University of Electronic Technology, Guilin, 541004, China

    Jingwei Zhang

  3. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China

    Jingwei Zhang

  4. College of Computer Science and Technology, Shanghai University of Electronic Power, Shanghai, 200090, China

    Zhongqin Bi

Authors
  1. Qing Yang
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  2. Le Zhan
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  3. Li Han
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  4. Jingwei Zhang
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  5. Zhongqin Bi
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Corresponding author

Correspondence to Jingwei Zhang .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. University of West London, London, UK

    Xinheng Wang

  3. Hangzhou Dianzi University, Hangzhou Shi, Zhejiang, China

    Yuyu Yin

  4. London South Bank University, London, UK

    Muddesar Iqbal

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Yang, Q., Zhan, L., Han, L., Zhang, J., Bi, Z. (2019). Recommending More Suitable Music Based on Users’ Real Context. In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-12981-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12980-4

  • Online ISBN: 978-3-030-12981-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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