Performance Characterization of Game Recommendation Algorithms on Online Social Network Sites
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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.
Keywordsmining method and algorithm data mining personalization
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- 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.
- GP Bullhound. Social Gaming: the fastest growing segment of the games market. http://gpbullhound.com/en/research/, 2010.
- InfoSolutionsGroup. PopCap Social Gaming Research Results. http://www.infosolutionsgroup.com/2010 PopCap Social Gaming Research Results.pdf, 2010.
- Gatcha!. http://netlog.com/play, 2012.
- Netlog. http://netlog.com/go/about, 2012.
- 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.Google Scholar
- 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.Google Scholar
- Pessiot J F, Truong T V, Usunier N et al. Learning to rank for collaborative filtering. In Proc. ICEIS, Jun. 2007, pp.145–151.Google Scholar
- Liu N N, Yang Q. EigenRank: A ranking-oriented approach to collaborative filtering. In Proc. SIGIR, July 2008, pp.83–90.Google Scholar
- 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.Google Scholar
- Golbeck J. Generating predictive movie recommendations from trust in social networks. In Proc. the 4th iTrust, May 2006, pp.93–104.Google Scholar
- 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.Google Scholar
- Drachen A, Canossa A. Towards gameplay analysis via game-play metrics. In Proc. the 13th MindTrek, Sept. 30-Oct. 2, 2009, pp.202–209.Google Scholar
- 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.Google Scholar
- Lee W S. Collaborative learning for recommender systems. In Proc. the 18th ICML, June 2001, pp.314–321.Google Scholar
- Hu Y, Koren Y, Volinsky C. Collaborative filtering for implicit feedback datasets. In Proc. the 8th ICDM, Dec. 2008, pp.263–272.Google Scholar
- 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.Google Scholar
- Sarwar B, Karypis G, Konstan J, Reidl J. Item-based collaborative filtering recommendation algorithms. In Proc. the 10th WWW, May 2001, pp.285–295.Google Scholar