![](bg8.png)
Anwar, S. M., Shahzad, T., Sattar, Z., Khan, R., and Majid,
M. (2017). A game recommender system using col-
laborative filtering (GAMBIT). Proceedings of 2017
14th International Bhurban Conference on Applied
Sciences and Technology, IBCAST 2017, pages 328–
332.
Bertens, P., Guitart, A., Chen, P. P., and Perianez, A. (2018).
A Machine-Learning Item Recommendation System
for Video Games. In IEEE Conference on Compu-
tatonal Intelligence and Games, CIG, volume 2018-
Augus.
Buder, J. and Schwind, C. (2012). Learning with person-
alized recommender systems: A psychological view.
Computers in Human Behavior, 28(1):207–216.
Cheuque, G., Guzm
´
an, J., and Parra, D. (2019). Recom-
mender systems for online video game platforms: The
case of steam. The Web Conference 2019 - Compan-
ion of the World Wide Web Conference, WWW 2019,
2:763–771.
Hug, N. (2020). Surprise: A python library for recom-
mender systems. Journal of Open Source Software,
5(52):2174.
Jannach, D., Zanker, M., Felfernig, A., and Friedrich, G.
(2011). Recommendation system: An Introduction,
volume 91. Cambridge University Press, New York.
Joorabloo., N., Jalili., M., and Ren., Y. (2019). A new tem-
poral recommendation system based on users’ simi-
larity prediction. In Proceedings of the 11th Inter-
national Joint Conference on Knowledge Discovery,
Knowledge Engineering and Knowledge Management
- KDIR,, pages 555–560. INSTICC, SciTePress.
Koren, Y., Bell, R., and Volinsky, C. (2009). Matrix factor-
ization techniques for recommender systems. Com-
puter, 42(8):30–37.
Lemire, D. and Maclachlan, A. (2005). Slope one predictors
for online rating-based collaborative filtering. Pro-
ceedings of the 2005 SIAM International Conference
on Data Mining, SDM 2005, pages 471–475.
Luo, X., Zhou, M., Xia, Y., and Zhu, Q. (2014). An
efficient non-negative matrix-factorization-based ap-
proach to collaborative filtering for recommender sys-
tems. IEEE Transactions on Industrial Informatics,
10(2):1273–1284.
Mittal, A. and Subraveti, S. (2017). Comparison
of Recommendation Models On the Amazon Au-
tomotive Dataset. https://github.com/abhaymittal/
Recommendations-on-Amazon-Automotive-Dataset.
Parra, D. and Amatriain, X. (2011). Walk the Talk: Analyz-
ing the relation between implicit and explicit feedback
for preference elicitation. Proc. of the 19
th
interna-
tional conference on User Modeling, Adaption, and
Personalization, pages 255–268.
Pathak, A., Gupta, K., and McAuley, J. (2017). Generating
and personalizing bundle recommendations on steam.
SIGIR 2017 - Proceedings of the 40th International
ACM SIGIR Conference on Research and Develop-
ment in Information Retrieval, pages 1073–1076.
P
´
erez-Marcos, J., Mart
´
ın-G
´
omez, L., Jim
´
enez-Bravo,
D. M., L
´
opez, V. F., and Moreno-Garc
´
ıa, M. N.
(2020). Hybrid system for video game recommen-
dation based on implicit ratings and social networks.
Journal of Ambient Intelligence and Humanized Com-
puting.
Ricci, F., Rokach, L., Shapira, B., and Kantor, P. B. (2011).
Recommender Systems Handbook. Springer.
Sarwar, B. M., Konstan, J. A., Borchers, A., Herlocker,
J., Miller, B., and Riedl, J. (1998). Using filtering
agents to improve prediction quality in the grouplens
research collaborative filtering system. In Proc. of the
1998 ACM Conf. on Computer Supported Cooperative
Work, page 345–354. ACM.
Schafer, B. J., Frankowski, D., Herlocker, J., and Sen, S.
(2006). Collaborative filtering recommender systems.
Research Journal of Applied Sciences, Engineering
and Technology, 5(16):4168–4182.
Shah, K., Salunke, A., Dongare, S., and Antala, K. (2017).
Recommender systems: An overview of different ap-
proaches to recommendations. In Proceedings of 2017
International Conference on Innovations in Informa-
tion, Embedded and Communication Systems, ICI-
IECS 2017, pages 1–4.
Sifa, R., Drachen, A., and Bauckhage, C. (2015). Large-
scale cross-game player behavior analysis on steam.
Proceedings of the 11th AAAI Conference on Arti-
ficial Intelligence and Interactive Digital Entertain-
ment, AIIDE 2015, 2015-Novem:198–204.
Wang, D., Moh, M., and Moh, T. S. (2020). Using deep
learning and steam user data for better video game
recommendations. ACMSE 2020 - Proceedings of the
2020 ACM Southeast Conference, pages 154–159.
Yi, X., Hong, L., Zhong, E., Liu, N. N., and Rajan, S.
(2014). Beyond clicks: Dwell time for personaliza-
tion. RecSys 2014 - Proceedings of the 8th ACM Con-
ference on Recommender Systems, pages 113–120.
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