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Performance Characterization of Game Recommendation Algorithms on Online Social Network Sites

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Abstract

Since years, online social networks have evolved from profile and communication websites to online portals where people interact with each other, share and consume multimedia-enriched data and play different types of games. Due to the immense popularity of these online games and their huge revenue potential, the number of these games increases every day, resulting in a current offering of thousands of online social games. In this paper, the applicability of neighborhood-based collaborative filtering (CF) algorithms for the recommendation of online social games is evaluated. This evaluation is based on a large dataset of an online social gaming platform containing game ratings (explicit data) and online gaming behavior (implicit data) of millions of active users. Several similarity metrics were implemented and evaluated on the explicit data, implicit data and a combination thereof. It is shown that the neighborhood-based CF algorithms greatly outperform the content-based algorithm, currently often used on online social gaming websites. The results also show that a combined approach, i.e., taking into account both implicit and explicit data at the same time, yields overall good results on all evaluation metrics for all scenarios, while only slightly performing worse compared to the strengths of the explicit or implicit only approaches. The best performing algorithms have been implemented in a live setup of the online game platform.

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References

  1. Nielsen. Nielsen NetView: June 2009-June 2010. http://blog.nielsen.com/nielsenwire/online_mobile/what-americans-do-online-social-media-and-games-dominate-activity/, 2010.

  2. GP Bullhound. Social Gaming: the fastest growing segment of the games market. http://gpbullhound.com/en/research/, 2010.

  3. InfoSolutionsGroup. PopCap Social Gaming Research Results. http://www.infosolutionsgroup.com/2010 PopCap Social Gaming Research Results.pdf, 2010.

  4. Gatcha!. http://netlog.com/play, 2012.

  5. Netlog. http://netlog.com/go/about, 2012.

  6. Konstan J A, Miller B N, Maltz D, Herlocker J L, Gordon L R, Riedl J. GroupLens: Applying collaborative filtering to Usenet news. Commun. ACM, 1997, 40(3): 77–87.

    Article  Google Scholar 

  7. Bell R M, Koren Y. Lessons from the Netflix prize challenge. SIGKDD Explorations Newsletter, 2007, 9(2): 75–79.

    Article  Google Scholar 

  8. Leung C, Chan S, Chung F, Ngai G. A probabilistic rating inference framework for mining user preferences from reviews. World Wide Web, 2011, 14(2): 187–215.

    Article  Google Scholar 

  9. Go G, Yang J, Park H, Han S. Using online media sharing behavior as implicit feedback for collaborative filtering. In Proc. the 2nd SocialCom, Aug. 2010, pp.439–445.

  10. Shen D, Sun J T, Yang Q, Chen Z. A comparison of implicit and explicit links for web page classification. In Proc. the 15th WWW, May 2006, pp.643–650.

  11. Pessiot J F, Truong T V, Usunier N et al. Learning to rank for collaborative filtering. In Proc. ICEIS, Jun. 2007, pp.145–151.

  12. Liu N N, Yang Q. EigenRank: A ranking-oriented approach to collaborative filtering. In Proc. SIGIR, July 2008, pp.83–90.

  13. Liu N N, Xiang E W, Zhao M, Yang Q. Unifying explicit and implicit feedback for collaborative filtering. In Proc. the 19th CIKM, Oct. 2010, pp.1445–1448.

  14. Golbeck J. Generating predictive movie recommendations from trust in social networks. In Proc. the 4th iTrust, May 2006, pp.93–104.

  15. DuBois T, Golbeck J, Kleint J, Srinivasan A. Improving recommendation accuracy by clustering social networks with trust. In Proc. RecSys Workshop on Recommender Systems and the Social Web, Oct., 2009.

  16. Li H Q, Xia F, Zeng D et al. Exploring social annotations with the application to web page recommendation. Journal of Computer Science and Technology, 2009, 24(6): 1028–1034.

    Article  Google Scholar 

  17. Wang K, Zhou S, Yang Q, Yeung J M S. Mining customer value: From association rules to direct marketing. Data Mining and Knowledge Discovery, 2005, 11(1): 57–79.

    Article  MathSciNet  Google Scholar 

  18. Drachen A, Canossa A. Towards gameplay analysis via game-play metrics. In Proc. the 13th MindTrek, Sept. 30-Oct. 2, 2009, pp.202–209.

  19. Medler B, John M, Lane J. Data cracker: Developing a visual game analytic tool for analyzing online gameplay. In Proc. the 2011 CHI, May 2011, pp.2365–2374.

  20. Lee W S. Collaborative learning for recommender systems. In Proc. the 18th ICML, June 2001, pp.314–321.

  21. Hu Y, Koren Y, Volinsky C. Collaborative filtering for implicit feedback datasets. In Proc. the 8th ICDM, Dec. 2008, pp.263–272.

  22. Herlocker J L, Konstan J A, Borchers A, Riedl J. An algorithmic framework for performing collaborative filtering. In Proc. the 22nd SIGIR, Aug. 1999, pp.230–237.

  23. Sarwar B, Karypis G, Konstan J, Reidl J. Item-based collaborative filtering recommendation algorithms. In Proc. the 10th WWW, May 2001, pp.285–295.

  24. Lee T Q, Park Y, Park Y T. A time-based approach to effective recommender systems using implicit feedback. Expert Systems with Applications, 2008, 34(4): 3055–3062.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, Interdisciplinary Institute for Broadband Technology (IBBT), Ghent University, G. Crommenlaan 8/201, B-9050, Gent, Belgium

    Philip Leroux (Student Member, IEEE), Bart Dhoedt (Member, IEEE), Piet Demeester (Fellow, IEEE) & Filip De Turck (Senior Member, IEEE)

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  1. Philip Leroux
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  2. Bart Dhoedt
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  3. Piet Demeester
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  4. Filip De Turck
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Correspondence to Philip Leroux.

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Leroux, P., Dhoedt, B., Demeester, P. et al. Performance Characterization of Game Recommendation Algorithms on Online Social Network Sites. J. Comput. Sci. Technol. 27, 611–623 (2012). https://doi.org/10.1007/s11390-012-1248-6

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  • DOI: https://doi.org/10.1007/s11390-012-1248-6

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