Skip to main content
SpringerLink
Log in

Improving music recommendation by incorporating social influence

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In the past decades, a large number of music pieces are uploaded to the Internet every day through social networks, such as Last.fm, Spotify and YouTube, that concentrates on music and videos. We have been witnessing an ever-increasing amount of music data. At the same time, with the huge amount of online music data, users are facing an everyday struggle to obtain their interested music pieces. To solve this problem, music search and recommendation systems are helpful for users to find their favorite content from a huge repository of music. However, social influence, which contains rich information about similar interests between users and users’ frequent correlation actions, has been largely ignored in previous music recommender systems. In this work, we explore the effects of social influence on developing effective music recommender systems and focus on the problem of social influence aware music recommendation, which aims at recommending a list of music tracks for a target user. To exploit social influence in social influence aware music recommendation, we first construct a heterogeneous social network, propose a novel meta path-based similarity measure called WPC, and denote the framework of similarity measure in this network. As a step further, we use the topological potential approach to mine social influence in heterogeneous networks. Finally, in order to improve music recommendation by incorporating social influence, we present a factor graphic model based on social influence. Our experimental results on one real world dataset verify that our proposed approach outperforms current state-of-the-art music recommendation methods substantially.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. http://www.last.fm/.

  2. http://www.spotify.com.

  3. http://www.pandora.com.

  4. http://grooveshark.com/.

References

  1. Aizenberg N, Koren Y, Somekh O (2012) Build your own music recommender by modeling internet radio streams[C]. International Conference on World Wide Web. ACM, pp 1–10

  2. Althoff T, Jindal P, Leskovec J (2017) Online actions with offline impact: How online social networks influence online and offline user behavior[C]. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ACM, pp 537–546

  3. Ben-Elazar S, Lavee G, Koenigstein N et al (2017) Groove radio: A bayesian hierarchical model for personalized playlist Generation[C]. Tenth ACM international conference on web search and data mining. ACM, pp 445–453

  4. Bilanakos C, Sotiropoulos DN, Georgoula I et al (2015) Optimal influence strategies in social Networks[J]. Soc Sci Electron Publ 49(4):1–45

    Google Scholar 

  5. Chang H-Y, Huang S-C, Wu J-H (2017) A personalized music recommendation system based on electroencephalography feedback. Multimed Tools Appl 76.19:19523–19542

    Article  Google Scholar 

  6. Cheng Z, Shen J, Mei T (2014) Just-for-me: an adaptive personalization system for location-aware social music recommendation. SIGIR, pp 1267–1268

  7. Cheng Z, Shen J (2016) On effective location-aware music recommendation[J]. Acm Trans Inf Syst 34(2):1–32

    Article  MathSciNet  Google Scholar 

  8. Cheng Z, Shen J, Hoi SCH (2016) On effective personalized music retrieval by exploring online user Behaviors[C]. International ACM SIGIR conference on research and development in information retrieval. ACM, pp 125–134

  9. Cheng Z, Shen J, Nie L, Chua T-S, Kankanhalli M (2017) Exploring User-Specific Information in Music Retrieval. In proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, pp 655–664

  10. Cheng Z, Shen J, L Zhu et al (2017) Exploiting music play sequence for music recommendation[c] twenty-sixth international joint conference on artificial intelligence, pp 3654–3660

  11. Craw S, Horsburgh B, Massie S (2015) Music recommenders: user evaluation without real users?[C] International Conference on Artificial Intelligence. AAAI Press, pp 1749–1755

  12. Deng Z, Sang J, Xu C (2013) Personalized video recommendation based on cross-platform user modeling. ICME, pp 1–6

  13. Dieleman S, Schrauwen B (2013) Deep content-based music recommendation[C]. International Conference on Neural Information Processing Systems. curran Associates Inc., pp 2643–2651

  14. Ferwerda B, Schedl M (2014) Enhancing music recommender systems with personality information and emotional states: A proposal. In: Proceedings of UMAP

  15. Fujino H, Hasida K, Matsubara Y (2017) Music exploration by impression based Interaction[C] ACM workshop on exploratory search and interactive data analytics. ACM, pp 55–58

  16. Gillhofer M, Jogging SM (2015) Iron Maiden While Debussy for Dinner?[M] MultiMedia Modeling. Springer International Publishing, Berlin

    Google Scholar 

  17. Guo C, Liu X (2015) Automatic feature generation on heterogeneous graph for music recommendation[C] international ACM SIGIR conference on research and development in information retrieval. ACM, pp 807–810

  18. Hariri N, Mobasher B, Burke R (2012) Context-aware music recommendation based on latenttopic sequential patterns[C]. ACM Conference on Recommender Systems. ACM, pp 131–138

  19. Hung H-J, Shuai H-H, Yang D-N, Huang L-H, Lee WC, Pei J, Chen M-S (2016) When social influence meets item inference. In: Proceedings of the 22nd ACM sigkdd international conference on knowledge discovery and data mining. ACM, pp 915–924

  20. Ivanov S, Theocharidis K, Terrovitis M et al (2017) Content recommendation for viral social Influence[C]. International ACM SIGIR conference on research and development in information retrieval. ACM, pp 565–574

  21. Jannach D, Lerche L, Kamehkhosh I (2015) Beyond “hitting the hits”: generating coherent music playlist continuations with the right tracks[c], pp 187–194

  22. Kaminskas M, Ricci F, Schedl M (2013) Location-aware music recommendation using auto-tagging and hybrid matching[C]. ACM Conference on Recommender Systems. ACM, pp 17–24

  23. Knees P, Schedl M (2013) A survey of music similarity and recommendation from music context data[J]. Acm Trans Multimed Comput Commun Appl 10(1):1–21

    Article  Google Scholar 

  24. Lichtenwalter R, Lussier JT, Chawla NV (2010) New perspectives and methods in link prediction. In: Kdd’10, pp 243–252

  25. Lu L et al (2012) Learning influence from heterogeneous social networks. Data mining and knowledge discovery 25.3, pp 511–544

  26. Lu Y, Qiao Z, Zhang P et al (2017) Ranking-Based music recommendation in online music Radios[C]. IEEE international conference on data science in cyberspace. IEEE, pp 614–619

  27. Moshfeghi Y, Piwowarski B, Jose JM (2011) Handling data sparsity in collaborative filtering using emotion and semantic based features[C]. International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, pp 625–634

  28. Onori M, Micarelli A, Sansonetti G (2016) A comparative analysis of Personality-Based music recommender systems. In: Empire@ RecSys, pp 55–59

  29. Pálovics R, Benczúr A A, Kocsis L et al (2014) Exploiting temporal influence in online recommendation. Proceedings of the 8th ACM Conference on Recommender systems. ACM, pp 273–280

  30. Qi Q, Chen Z, Liu J et al Using inferred tag ratings to improve user-based collaborative filtering. ACM Symposium on Applied Computing. ACM, 2012:2008–2013

  31. Sang J, Mei T, Xu C (2015) Activity sensor: Check-in usage mining for local recommendation[m]. ACM TIST 6(3):41:1–41:24

    Google Scholar 

  32. Schedl M, Vall A, Farrahi K (2014) User geospatial context for music recommendation in microblogs[C]. International ACM SIGIR Conference on Research andamp; Development in Information Retrieval. ACM, pp 987–990

  33. Schedl M, Hauger D (2015) Tailoring music recommendations to users by considering diversity, mainstreaminess, and Novelty[C]. International ACM SIGIR conference on research and development in information retrieval. ACM, pp 947–950

  34. Schedl M, Hauger D, Farrahi K et al (2015) On the influence of user characteristics on music recommendation Algorithms[C]. European conference on ir research, ECIR 2015, proceedings, pp 339–345

  35. Seo YD, Kim YG, Lee E et al (2017) Personalized recommender system based on friendship strength in social network services[J]. Expert Syst Appl 69:135–148

    Article  Google Scholar 

  36. Su JH, Chang WY, Tseng VS (2017) Effective social content-based collaborative filtering for music recommendation[J]. Intell Data Anal 21:S195–S216

    Article  Google Scholar 

  37. Sun J, Tang J (2011) A survey of models and algorithms for social influence Analysis[M]. Social network data analytics. Springer, US

    Google Scholar 

  38. Tang W, Zhuang H, Tang J (2011) Learning to infer social ties in large networks[c]. Joint european conference on machine learning and knowledge discovery in databases. Springer, Berlin

    Google Scholar 

  39. Ugander J, Backstrom L, Marlow C et al (2012) Structural diversity in social contagion[J]. Proc Natl Acad Sci US Amer 109(16):5962

    Article  Google Scholar 

  40. Wang X, Wang Y (2014) Improving Content-based and Hybrid Music Recommendation using Deep Learning[C]. ACM International Conference on Multimedia. ACM, pp 627–636

  41. Wang D, Liu S, Liu S et al (2016) Improving music recommendation using distributed Representation[C]. International conference companion on world wide web. international world wide web conferences steering committee, pp 125–126

  42. Wen YT, Fan YY (2017) Peng w c mining of Location-Based social networks for Spatio-Temporal social Influence[M]. Advances in knowledge discovery and data mining

  43. Yan M, Sang J, Xu C (2015) Unified YouTube Video Recommendation via Cross-network Collaboration[C]. ACM on International Conference on Multimedia Retrieval. ACM, pp 19–26

  44. Zhang J, Liu B, Tang J et al (2013) Social influence locality for modeling retweeting behaviors[C]. International Joint Conference on Artificial Intelligence. AAAI press, pp 2761–2767

  45. Zheng Z, Ma H, Lyu MR et al (2013) Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization[J]. IEEE Trans Serv Comput 6 (3):289–299

    Article  Google Scholar 

  46. Zhang J, Tang J, Li J et al (2015) Who influenced you? Predicting retweet via social influence locality[J]. ACM Trans Knowl Discov Data (TKDD) 9(3):25

    Google Scholar 

  47. Zhang Q, Wu J, Yang H et al (2016) Global and Local Influence-based Social Recommendation[C]. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, pp 1917–1920

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant No.61702043, and the Fundamental Research Funds for the Central Universities under Grant No. 500417062.

Author information

Authors and Affiliations

  1. School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Jinpeng Chen

  2. School of WTO Research and Education, Shanghai University of International Business and Economics, Shanghai, 200336, China

    Pinguang Ying

  3. State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China

    Ming Zou

Authors
  1. Jinpeng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pinguang Ying
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ming Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinpeng Chen.

Additional information

Fully documented templates are available in the elsarticle package on CTAN.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Ying, P. & Zou, M. Improving music recommendation by incorporating social influence. Multimed Tools Appl 78, 2667–2687 (2019). https://doi.org/10.1007/s11042-018-5745-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-5745-7

Keywords

Search

Navigation