Abstract
Video games are a relatively new form of entertainment that has been rapidly gaining popularity in recent years. The number of video games available to users is huge and constantly growing, and thus it can be a daunting task to search for new ones to play. Given that some games are designed to be played together as a group, finding games suitable for the whole group can be even more challenging. To counter this problem, we propose a content-based video game recommender system, GameRecs, which works on open data gathered from Steam, a popular digital distribution platform. GameRecs is capable of producing both user profiles based on Steam’s user data, as well as video game recommendations for those profiles. It generates group recommendations by exploiting lists aggregation methods, and focus on providing suggestions that exhibit some diversity by using a k-means clustering-based approach. We have evaluated the usability of GameRecs in terms of the user profile generation and the produced video game recommendations, both for single users and for groups. For group recommendations we compared two recommendation aggregation methods, Borda Count and Least Misery method. For diversity evaluation we compared results with and without the proposed k-means clustering method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Our application implementation and the data used in the experiments are publicly available at https://github.com/Nikkilae/group-game-recommender-test.
References
Adomavicius, G., Kwon, Y.: Multi-criteria recommender systems. In: Recommender Systems Handbook, pp. 847–880 (2015)
Balabanovic, M., Shoham, Y.: Content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)
Beel, J., Langer, S., Genzmehr, M., Nürnberger, A.: Persistence in recommender systems: giving the same recommendations to the same users multiple times. In: Aalberg, T., Papatheodorou, C., Dobreva, M., Tsakonas, G., Farrugia, C.J. (eds.) TPDL 2013. LNCS, vol. 8092, pp. 386–390. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40501-3_43
Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 107–144. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_4
Eirinaki, M., Abraham, S., Polyzotis, N., Shaikh, N.: Querie: collaborative database exploration. IEEE Trans. Knowl. Data Eng. 26(7), 1778–1790 (2014)
Ge, X., Chrysanthis, P.K., Pelechrinis, K.: MPG: not so random exploration of a city. In: MDM (2016)
Koskela, M., Simola, I., Stefanidis, K.: Open source software recommendations using Github. In: Méndez, E., Crestani, F., Ribeiro, C., David, G., Lopes, J.C. (eds.) TPDL 2018. LNCS, vol. 11057, pp. 279–285. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00066-0_24
McAuley, J.J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: RecSys (2013)
Ntoutsi, E., Stefanidis, K., Rausch, K., Kriegel, H.: Strength lies in differences: diversifying friends for recommendations through subspace clustering. In: CIKM (2014)
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: The Adaptive Web, Methods and Strategies of Web Personalization (2007)
Sandvig, J.J., Mobasher, B., Burke, R.D.: A survey of collaborative recommendation and the robustness of model-based algorithms. IEEE Data Eng. Bull. 31(2), 3–13 (2008)
Sifa, R., Bauckhage, C., Drachen, A.: Archetypal game recommender systems. In: Proceedings of the 16th LWA Workshops (2014)
Stefanidis, K., Kondylakis, H., Troullinou, G.: On recommending evolution measures: a human-aware approach. In: 33rd IEEE International Conference on Data Engineering, ICDE 2017, San Diego, CA, USA, 19–22 April 2017, pp. 1579–1581 (2017)
Stefanidis, K., Ntoutsi, E., Kondylakis, H., Velegrakis, Y.: Social-based collaborative filtering. In: Alhajj, R., Rokne, J. (eds.) Encyclopedia of Social Network Analysis and Mining, pp. 1–9. Springer, New York (2017). https://doi.org/10.1007/978-1-4614-7163-9
Stratigi, M., Kondylakis, H., Stefanidis, K.: Fairness in group recommendations in the health domain. In: ICDE (2017)
Stratigi, M., Kondylakis, H., Stefanidis, K.: FairGRecs: fair group recommendations by exploiting personal health information. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R.R. (eds.) DEXA 2018. LNCS, vol. 11030, pp. 147–155. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98812-2_11
Yin, Z., Gupta, M., Weninger, T., Han, J.: LINKREC: a unified framework for link recommendation with user attributes and graph structure. In: WWW (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Hannula, R., Nikkilä, A., Stefanidis, K. (2019). GameRecs: Video Games Group Recommendations. In: Welzer, T., et al. New Trends in Databases and Information Systems. ADBIS 2019. Communications in Computer and Information Science, vol 1064. Springer, Cham. https://doi.org/10.1007/978-3-030-30278-8_49
Download citation
DOI: https://doi.org/10.1007/978-3-030-30278-8_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30277-1
Online ISBN: 978-3-030-30278-8
eBook Packages: Computer ScienceComputer Science (R0)