Research PaperOpen Access

Investigating Recommendation Algorithms for Escape Rooms

Sagi Bazinin

Software and Information Systems Engineering Department, Ben Gurion University of the Negev, Beer Sheva, Israel

E-mail Address: sagibaz@post.bgu.ac.il

Search for more papers by this author

and

Guy Shani

Software and Information Systems Engineering Department, Ben Gurion University of the Negev, Beer Sheva, Israel

E-mail Address: shanigu@bgu.ac.il

Search for more papers by this author

https://doi.org/10.1142/S2196888819500209Cited by:1 (Source: Crossref)

Abstract

An escape room is a physical puzzle solving game, where participants solve a series of riddles within a limited time to exit a locked room. Escape rooms differ in their theme, environment, and difficulty, and people hence often differ on their preferences over escape rooms. As such, recommendation systems can help people in deciding which room to visit. In this paper, we describe the properties of the escape rooms recommendation problem, with respect to other popular recommendation problems. We describe a dataset of reviews collected within a current system. We provide an empirical comparison between a set of recommendation algorithms over two problems, top-N recommendation and rating prediction. In both cases, a KNN method performed the best.

Keywords:

References

1. F. Amat, A. Chandrashekar, T. Jebara and J. Basilico, Artwork personalization at netflix, in Proc. 12th ACM Conf. Recommender Systems (ACM, 2018), pp. 487–488. Crossref, Google Scholar
2. O. Barkan and N. Koenigstein, Item2vec: Neural item embedding for collaborative filtering, 2016 IEEE 26th Int. Workshop Machine Learning for Signal Processing (MLSP) (IEEE, 2016), pp. 1–6. Crossref, Google Scholar
3. J. Borràs, A. Moreno and A. Valls, Intelligent tourism recommender systems: A survey, Expert Syst. Appl. 41(16) (2014) 7370–7389. Crossref, Google Scholar
4. J. S. Breese, D. Heckerman and C. Kadie, Empirical analysis of predictive algorithms for collaborative filtering, in Proc. 14th Conf. Uncertainty in Artificial Intelligence (Morgan Kaufmann Publishers Inc., 1998), pp. 43–52. Google Scholar
5. L. Chen, G. Chen and F. Wang, Recommender systems based on user reviews: The state of the art, User Model. User-Adapt. Inter. 25(2) (2015) 99–154. Crossref, Google Scholar
6. C. Cheng, H. Yang, M. R. Lyu and I. King, Where you like to go next: Successive point-of-interest recommendation, in Int. Joint Conf. Artif. Intell. 13 (2013) 2605–2611. Google Scholar
7. C. Desrosiers and G. Karypis, A comprehensive survey of neighborhood-based recommendation methods, in Recommender Systems Handbook (Springer, 2011), pp. 107–144. Crossref, Google Scholar
8. H. Drachsler, K. Verbert, O. C. Santos and N. Manouselis, Panorama of recommender systems to support learning, in Recommender Systems Handbook (Springer, 2015), pp. 421–451. Crossref, Google Scholar
9. M. D. Ekstrand, J. T. Riedl, J. A. Konstan et al., Collaborative filtering recommender systems, Found. Trends® Human–Comput. Interact. 4(2) (2011) 81–173. Crossref, Google Scholar
10. S. Feng, X. Li, Y. Zeng, G. Cong, Y. M. Chee and Q. Yuan, Personalized ranking metric embedding for next new poi recommendation, in Int. Joint Conf. Artif. Intell. 15 (2015) 2069–2075. Google Scholar
11. Z. Gantner, L. Drumond, C. Freudenthaler and L. Schmidt-Thieme, Personalized ranking for non-uniformly sampled items, in Proc. KDD Cup (PMLR, 2011), pp. 231–247. Google Scholar
12. Z. Gantner, S. Rendle, C. Freudenthaler and L. Schmidt-Thieme, MyMediaLite: A free recommender system library, in Proc. 5th ACM Conf. Recommender Systems (RecSys 2011). ACM, pp. 305–308. Google Scholar
13. T. George and S. Merugu, A scalable collaborative filtering framework based on co-clustering, Fifth IEEE Int. Conf. Data Mining, 27–30 November 2005, Hauston, TX, pp. 625–628. Google Scholar
14. F. M. Harper and J. A. Konstan, The movielens datasets: History and context, ACM Trans. Interactive Intell. Syst. 5(4) (2016) 19. Google Scholar
15. N. Hug, Surprise, a Python library for recommender systems, http://surpriselib.com. Google Scholar
16. D. Jannach, L. Lerche and M. Jugovac, Adaptation and evaluation of recommendations for short-term shopping goals, in Proc. 9th ACM Conf. Recommender Systems (ACM, 2015), pp. 211–218. Crossref, Google Scholar
17. S. Jiang, X. Qian, T. Mei and Y. Fu, Personalized travel sequence recommendation on multi-source big social media, IEEE Trans. Big Data 2(1) (2016) 43–56. Crossref, Google Scholar
18. T. Kolar, Conceptualising tourist experiences with new attractions: The case of escape rooms, Int. J. Contemp. Hospital. Manag. 29(5) (2017) 1322–1339. Crossref, Google Scholar
19. Y. Koren, Factorization meets the neighborhood: A multifaceted collaborative filtering model, in Proc. 14th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (ACM, 2008), pp. 426–434. Crossref, Google Scholar
20. Y. Koren, Factor in the neighbors: Scalable and accurate collaborative filtering, ACM Trans. Knowl. Discov. Data 4(1) (2010) 1. Crossref, Google Scholar
21. Y. Koren and R. Bell, Advances in collaborative filtering, Recommender Systems Handbook (Springer, 2015), pp. 77–118. Crossref, Google Scholar
22. P. Lops, M. De Gemmis and G. Semeraro, Content-based recommender systems: State of the art and trends, in Recommender Systems Handbook (Springer, 2011), pp. 73–105. Crossref, Google Scholar
23. J. Lu, D. Wu, M. Mao, W. Wang and G. Zhang, Recommender system application developments: A survey, Decis. Support Syst. 74 (2015) 12–32. Crossref, Google Scholar
24. X. Luo, M. Zhou, Y. Xia and Q. Zhu, An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems, IEEE Trans. Ind. Inform. 10(2) (2014) 1273–1284. Crossref, Google Scholar
25. J. Masthoff, Group recommender systems: Combining individual models, in Recommender Systems Handbook (Springer, 2011), pp. 677–702. Crossref, Google Scholar
26. A. S. Mattila, How affective commitment boosts guest loyalty (and promotes frequent-guest programs), Cornell Hotel Restaurant Adm. Q. 47(2) (2006) 174–181. Crossref, Google Scholar
27. A. G. Mauri and R. Minazzi, Web reviews influence on expectations and purchasing intentions of hotel potential customers, Int. J. Hospital. Manag. 34 (2013) 99–107. Crossref, Google Scholar
28. S. Nicholson, Peeking behind the locked door: A survey of escape room facilities, White Paper available online at http://scottnicholson.com/pubs/erfacwhite. pdf (2015). Google Scholar
29. X. Ning, C. Desrosiers and G. Karypis, A comprehensive survey of neighborhood-based recommendation methods, Recommender Systems Handbook (Springer, 2015), pp. 37–76. Crossref, Google Scholar
30. A. Paterek, Improving regularized singular value decomposition for collaborative filtering, in Proc. KDD Cup and Workshop, Vol. 2007 (ACM, 2007), pp. 5–8. Google Scholar
31. P. K. Ponnamma Divakaran and S. Nørskov, Are online communities on par with experts in the evaluation of new movies? evidence from the fandango community, Inf. Technol. People 29(1) (2016) 120–145. Crossref, Google Scholar
32. S. Rendle, C. Freudenthaler, Z. Gantner and L. Schmidt-Thieme, Bpr: Bayesian personalized ranking from implicit feedback, in Proc. Twenty-Fifth Conf. Uncertainty Artif. Intell. (AUAI Press, 2009), pp. 452–461. Google Scholar
33. F. Ricci, L. Rokach and B. Shapira, Introduction to recommender systems handbook, in Recommender Systems Handbook (Springer, 2011), pp. 1–35. Crossref, Google Scholar
34. F. Ricci, L. Rokach and B. Shapira, Recommender systems: Introduction and challenges, in Recommender Systems Handbook (Springer, 2015), pp. 1–34, https://doi.org/10.1007/978-1-4899-7637-6_1, https://doi.org/10.1007/978-1-4899-7637-6_1. Crossref, Google Scholar
35. B. Sarwar, G. Karypis, J. Konstan and J. Riedl, Item-based collaborative filtering recommendation algorithms, in Proc. 10th Int. Conf. World Wide Web (ACM, 2001), pp. 285–295. Crossref, Google Scholar
36. G. Shani and A. Gunawardana, Evaluating recommendation systems, in Recommender Systems Handbook (Springer, 2011), pp. 257–297. Crossref, Google Scholar
37. G. Shani, D. Heckerman and R. I. Brafman, An mdp-based recommender system, J. Mach. Learn. Res. 6(Sep) (2005) 1265–1295. Google Scholar
38. B. Smith and G. Linden, Two decades of recommender systems at amazon. com, IEEE Internet Comput. 21(3) (2017) 12–18. Crossref, Google Scholar
39. M. Wiemker, E. Elumir and A. Clare, Escape room games, Game Based Learn. 55 (2015) 55–68. Google Scholar

Vol. 06, No. 04

Metrics

Downloaded 825 times

History

Received 15 May 2019

Accepted 28 June 2019

Published: 25 July 2019

Information

This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Keywords

PDF download

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

Investigating Recommendation Algorithms for Escape Rooms

Abstract

Recommended