[Adomavicius, G., Bockstedt, J., Shawn, C., and Zhang, J. (2014). De-biasing user preference ratings in recommender systems. CEUR Workshop Proceedings.]Search in Google Scholar
[Armstrong, R. A. (2014). When to use the Bonferroni correction. Ophthalmic & Physiological Optics: The Journal of the British College of Ophthalmic Opticians (Optometrists), 34(5), 502-508.10.1111/opo.12131]Search in Google Scholar
[Bouckaert, R. R., and Frank, E. (2004). Evaluating the replicability of significance tests for comparing learning algorithms. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3056, 3-12.10.1007/978-3-540-24775-3_3]Search in Google Scholar
[Brynjolfsson, E., and McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York, London: WW Norton & Company.]Search in Google Scholar
[Conover, W. J., and Iman, R. L. (1979). On multiple-comparisons procedures. Los Alamos Scientific Laboratory Tech. Rep. LA-7677-MS, 1(14).]Search in Google Scholar
[De Myttenaere, A., Grand, B. Le, Golden, B., and Rossi, F. (2014). Reducing offline evaluation bias in recommendation systems. ArXiv Preprint ArXiv:1407.0822.]Search in Google Scholar
[Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7(Jan), 1-30.]Search in Google Scholar
[García, S., Fernández, A., Luengo, J., and Herrera, F. (2009). A study of statistical techniques and performance measures for genetics-based machine learning: Accuracy and interpretability. Soft Computing, 13(10), 959-977.10.1007/s00500-008-0392-y]Search in Google Scholar
[Goldberg, D., Nichols, D., Oki, B. M., and Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61-70.10.1145/138859.138867]Search in Google Scholar
[Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. Cambridge Massachusetts, London: MIT Press.]Search in Google Scholar
[He, R., and McAuley, J. (2016). Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In 25th International World Wide Web Conference, WWW 2016.10.1145/2872427.2883037]Search in Google Scholar
[He, X., Du, X., Wang, X., Tian, F., Tang, J., and Chua, T.-S. (2018). Outer product-based neural collaborative filtering. ArXiv Preprint ArXiv:1808.03912.]Search in Google Scholar
[He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T.-S. (2017). Neural collaborative filtering. Proceedings of the 26th International Conference on World Wide Web, 173-182.10.1145/3038912.3052569]Search in Google Scholar
[Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 65-70.]Search in Google Scholar
[Hornik, K., Stinchcombe, M., White, H., et al. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359-366.10.1016/0893-6080(89)90020-8]Search in Google Scholar
[Jones, M. T. (2013). Recommender systems. Part 1: Introduction to approaches and algorithms. IBM DeveloperWorks, 12.]Search in Google Scholar
[Khattar, D., Kumar, V., Gupta, M., and Varma, V. (2018). Neural Content-collaborative filtering for news recommendation. NewsIR@ ECIR, 2079, 45-50.]Search in Google Scholar
[Krishnan, S., Patel, J., Franklin, M. J., and Goldberg, K. (2014). A methodology for learning, analyzing, and mitigating social influence bias in recommender systems. RecSys 2014 – Proceedings of the 8th ACM Conference on Recommender Systems.10.1145/2645710.2645740]Search in Google Scholar
[Lam, X. N., Vu, T., Le, T. D., and Duong, A. D. (2008). Addressing the cold-start problem in recommendation systems. Proceedings of the 2nd international conference on Ubiquitous information management and communication, 208-211.10.1145/1352793.1352837]Search in Google Scholar
[Li, X., and She, J. (2017). Collaborative variational autoencoder for recommender systems. Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, 305-314.10.1145/3097983.3098077]Search in Google Scholar
[Lundberg, S. M., and Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems.]Search in Google Scholar
[McAuley, J., Targett, C., Shi, Q., and Van Den Hengel, A. (2015). Image-based recommendations on styles and substitutes. SIGIR 2015 – Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval.10.1145/2766462.2767755]Search in Google Scholar
[Moore, A. W., and Lee, M. S. (1994). Efficient algorithms for minimizing cross validation error. Machine Learning Proceedings 1994, 190-198.10.1016/B978-1-55860-335-6.50031-3]Search in Google Scholar
[Ni, J., Li, J., and McAuley, J. (2020). Justifying recommendations using distantly-labeled reviews and fine-grained aspects. EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference.]Search in Google Scholar
[Nielsen, M. A. (2015). Neural networks and deep learning (Vol. 25). Determination Press USA.]Search in Google Scholar
[Pereira, D. G., Afonso, A., and Medeiros, F. M. (2015). Overview of Friedman’s Test and post-hoc analysis. Communications in Statistics: Simulation and Computation, 44(10), 2636-2653.10.1080/03610918.2014.931971]Search in Google Scholar
[Qi, J., Du, J., Siniscalchi, S. M., Ma, X., and Lee, C.-H. (2020). On mean absolute error for deep neural network based vector-to-vector regression. IEEE Signal Processing Letters.10.1109/LSP.2020.3016837]Search in Google Scholar
[Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt-Thieme, L. (2012). BPR: Bayesian personalized ranking from implicit feedback. ArXiv Preprint ArXiv:1205.2618.]Search in Google Scholar
[Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. (1994). GroupLens: An open architecture for collaborative filtering of netnews. Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, CSCW 1994.10.1145/192844.192905]Search in Google Scholar
[Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). Why should I trust you? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 1135-1144.10.1145/2939672.2939778]Search in Google Scholar
[Sarwar, B., Karypis, G., Konstan, J., and Reidl, J. (2001). Item-based collaborative filtering recommendation algorithms. Proceedings of the tenth international conference on World Wide Web – WWW ’01, 285-295.10.1145/371920.372071]Search in Google Scholar
[Schafer, J., Frankowski, D., Herlocker, J., and Sen, S. (2007). Collaborative filtering recommender systems. The Adaptive Web (4321), 91-324.10.1007/978-3-540-72079-9_9]Search in Google Scholar
[Schaffer, C. (1993). Selecting a classification method by cross-validation. Machine Learning, 13(1), 135-143.10.1007/BF00993106]Search in Google Scholar
[Sedhain, S., Menon, A. K., Sanner, S., and Xie, L. (2015). Autorec: Autoencoders meet collaborative filtering. Proceedings of the 24th international conference on World Wide Web, 111-112.10.1145/2740908.2742726]Search in Google Scholar
[Shani, G., and Gunawardana, A. (2011). Evaluating Recommendation Systems. In F. Ricci, L. Rokach, B. Shapira, and P. Kantor (Eds.), Recommender systems handbook. Boston, MA.: Springer. https://doi.org/10.1007/978-0-387-85820-3_810.1007/978-0-387-85820-3_8]Search in Google Scholar
[Shardanand, U., and Maes, P. (1995). Social information filtering: algorithms for automating “word of mouth.” Proceedings of the SIGCHI conference on Human factors in computing systems, 210-217.10.1145/223904.223931]Search in Google Scholar
[Strub, F., Gaudel, R., and Mary, J. (2016). Hybrid recommender system based on autoencoders. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, 11-16.10.1145/2988450.2988456]Search in Google Scholar
[Trawinski, B., Smetek, M., Telec, Z., and Lasota, T. (2012). Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms. International Journal of Applied Mathematics and Computer Science, 22(4), 867-881.10.2478/v10006-012-0064-z]Search in Google Scholar
[Zhang, H., Shen, F., Liu, W., He, X., Luan, H., and Chua, T.-S. (2016). Discrete collaborative filtering. Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 325-334.10.1145/2911451.2911502]Search in Google Scholar