Advertisement

Personalized Product Recommendation for Interactive Media

  • Hal James Cooper
  • Garud Iyengar
  • Ching-Yung Lin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 903)

Abstract

The video game industry is larger than both the film and music industries combined yet has received scant academic attention. We explore recommendations that makes use of interactivity, arguably the most distinguishing feature of video game products. We show that implicit data that tracks user-game interactions and levels of attainment (e.g. Microsoft Xbox Achievements) has high predictive value when making recommendations. Furthermore, we argue that the characteristics of the video gaming hobby (low cost, high duration, socially relevant) make clear the necessity of personalized, individual recommendations that can incorporate social networking information. We tackle this problem from the viewpoint of graph querying and demonstrate the foundation of a new approach for learning structured graph queries from data.

Keywords

Social recommendation Human-centered computing 

References

  1. 1.
    Song, Y., Dixon, S., Pearce, M.: A survey of music recommendation systems and future perspectives. In: 9th International Symposium on Computer Music Modeling and Retrieval (2012)Google Scholar
  2. 2.
    Gomez-Uribe, C.A., Hunt, N.: The netflix recommender system. ACM Trans. Manag. Inf. Syst. 6, 1–19 (2015)CrossRefGoogle Scholar
  3. 3.
    Aggarwal, C.C.: Recommender Systems. Springer International Publishing (2016)Google Scholar
  4. 4.
  5. 5.
    Microsoft: Xbox Live—Xbox. https://www.xbox.com/en-US/live
  6. 6.
    Kuchera, B.: The Anatomy of a Review Bombing Campaign – Polygon. https://www.polygon.com/2017/10/4/16418832/pubg-firewatch-steam-review-bomb
  7. 7.
    Grayson, N.: Total War Game Gets Review Bombed On Steam Over Women Generals. https://steamed.kotaku.com/total-war-game-gets-review-bombed-on-steam-over-women-g-1829283785
  8. 8.
    Becker, R., Chernihov, Y., Shavitt, Y., Zilberman, N.: An analysis of the steam community network evolution. In: 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel (IEEEI), pp. 1–5 (2012)Google Scholar
  9. 9.
    Blackburn, J., et al.: Cheaters in the Steam Community Gaming Social Network. ArXiv e-prints (2011)Google Scholar
  10. 10.
    O’Neill, M., Vaziripour, E., Wu, J., Zappala, D.: Condensing steam: distilling the diversity of gamer behavior. In: Proceedings of 2016 ACM Internet Measurement Conference, pp. 81–95 (2016)Google Scholar
  11. 11.
    Jakobsson, M.: The achievement machine: understanding Xbox 360 achievements in gaming practices. Game Stud. 11, 1–22 (2011)Google Scholar
  12. 12.
    Niizumi, H., Thorsen, T.: PlayStation Network Platform Detailed (2006). https://www.gamespot.com/articles/playstation-network-platform-detailed/1100-6145981/
  13. 13.
    Hamari, J.: Framework for designing and evaluating game achievements. In: Proc. DiGRA 2011 Conference: Think Design Play, p. 20 (2011)Google Scholar
  14. 14.
    Kumar, R., Verma, B.K., Sunder Rastogi, S.: Social Popularity based SVD++ Recommender System. Int. J. Comput. Appl. 87, 33–37 (2014)Google Scholar
  15. 15.
    Hug, N.: Surprise, a Python library for recommender systems (2017). http://surpriselib.com
  16. 16.
    Tang, J., Hu, X., Liu, H.: Social recommendation: a review. Soc. Netw. Anal. Min. (2013)Google Scholar
  17. 17.
    Konstas, I., Stathopoulos, V., Jose, J.M.: On Social networks and collaborative recommendation. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (2009)Google Scholar
  18. 18.
    Bonifati, A., Ciucanu, R., Lemay, A.: Learning path queries on graph databases. In: 18th International Conference on Extending Database Technology (EDBT) (2015)Google Scholar
  19. 19.
    Barceló, P., Libkin, L., Lin, A.W., Wood, P.T.: Expressive languages for path queries over graph-structured data. ACM Trans. Datab. Syst. 37, 31:1–31:46 (2012)Google Scholar
  20. 20.
    Arenas, M., Diaz, G.I., Kostylev, E. V.: Reverse engineering SPARQL queries. In: Proceedings of the 25th International Conference on World Wide Web – WWW 2016 (2016)Google Scholar
  21. 21.
    Angles, R., Arenas, M., Barceló, P., Hogan, A., Reutter, J., Vrgoč, D.: Foundations of modern query languages for graph databases. ACM Comput. Surv. 50, 68 (2017)CrossRefGoogle Scholar
  22. 22.
    Maddison, C.J., Mnih, A., Teh, Y.W.: The concrete distribution: a continuous relaxation of discrete random variables. arXiv Preprint arXiv1611.00712 (2016)Google Scholar
  23. 23.
    Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv Preprint arXiv1312.6114 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hal James Cooper
    • 1
  • Garud Iyengar
    • 1
  • Ching-Yung Lin
    • 2
  1. 1.Columbia UniversityNew YorkUSA
  2. 2.Graphen, Inc.New YorkUSA

Personalised recommendations