Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems

Author:

Wegmeth LukasORCID,Vente TobiasORCID,Purucker LennartORCID

Publisher

Springer Nature Switzerland

Reference57 articles.

1. Abdollahpouri, H., Burke, R., Mobasher, B.: Managing popularity bias in recommender systems with personalized re-ranking. arXiv preprint arXiv:1901.07555 (2019)

2. Anand, R., Beel, J.: Auto-surprise: an automated recommender-system (autorecsys) library with tree of parzens estimator (TPE) optimization. In: Proceedings of the 14th ACM Conference on Recommender Systems (RecSys 2020), pp. 585–587. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3383313.3411467

3. Anelli, V.W., et al.: Elliot: a comprehensive and rigorous framework for reproducible recommender systems evaluation. In: Diaz, F., Shah, C., Suel, T., Castells, P., Jones, R., Sakai, T. (eds.) The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event (SIGIR 2021), Canada, 11–15 July 2021, pp. 2405–2414. ACM (2021). https://doi.org/10.1145/3404835.3463245

4. Barkan, O., Hirsch, R., Katz, O., Caciularu, A., Koenigstein, N.: Anchor-based collaborative filtering. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM 2021), pp. 2877–2881. Association for Computing Machinery, New York (2021). https://doi.org/10.1145/3459637.3482056

5. Beel, J., Dinesh, S.: Real-world recommender systems for academia: the pain and gain in building, operating, and researching them. In: Mayr, P., Frommholz, I., Cabanac, G. (eds.) Proceedings of the Fifth Workshop on Bibliometric-enhanced Information Retrieval (BIR) Co-located with the 39th European Conference on Information Retrieval (ECIR 2017), Aberdeen, UK, 9th April 2017. CEUR Workshop Proceedings, vol. 1823, pp. 6–17. CEUR-WS.org (2017). https://ceur-ws.org/Vol-1823/paper1.pdf

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