Open Access

Improvement of E-Commerce Recommendation Systems with Deep Hybrid Collaborative Filtering with Content: A Case Study


Cite

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.12131Search 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_3Search 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-ySearch 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), 61-70.10.1145/138859.138867Search 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 2016.10.1145/2872427.2883037Search 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, 173-182.10.1145/3038912.3052569Search 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)90020-8Search 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.10.1145/2645710.2645740Search 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, 208-211.10.1145/1352793.1352837Search 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, 305-314.10.1145/3097983.3098077Search 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.10.1145/2766462.2767755Search 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/B978-1-55860-335-6.50031-3Search 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), 2636-2653.10.1080/03610918.2014.931971Search 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.2020.3016837Search 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 1994.10.1145/192844.192905Search 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, 1135-1144.10.1145/2939672.2939778Search 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, 285-295.10.1145/371920.372071Search 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_9Search in Google Scholar

Schaffer, C. (1993). Selecting a classification method by cross-validation. Machine Learning, 13(1), 135-143.10.1007/BF00993106Search 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, 111-112.10.1145/2740908.2742726Search 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_8Search 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, 210-217.10.1145/223904.223931Search 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, 11-16.10.1145/2988450.2988456Search 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-zSearch 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, 325-334.10.1145/2911451.2911502Search in Google Scholar

eISSN:
2449-9994
Language:
English