Abstract
Neural Collaborative Filtering recommendations are traditionally based on regression architectures (returning continuous predictions, e.g. 2.8 stars), such as DeepMF and NCF. However, there are advantages in the use of collaborative filtering classification models. This work tested both neuronal approaches using a set of representative open datasets, baselines, and quality measures. The results show the superiority of the regular regression model compared to the regular classification model (returning discrete predictions, e.g. 1–5 stars) and the binary classification model (returning binary predictions: recommended, non-recommended). Results also show a similar performance when comparing our proposed recommendation neural approach with the state-of-the-art neural regression baseline. The key issue is the additional information the recommendation approach provides compared to the regression model: While the regression baseline only returns the recommendation values, the proposed recommendation model returns \(\langle \)value, probability\(\rangle \) pairs. Extra probability information can be used in the recommender systems area for different objectives: recommendation explanation, visualization of results, quality improvements, mitigate attack risks, etc.
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References
Bobadilla, J., González-Prieto, Á., Ortega, F., Lara-Cabrera, R.: Deep learning feature selection to unhide demographic recommender systems factors. Neural Comput. Appl. 33(12), 7291–7308 (2021)
Bobadilla, J., Gutiérrez, A., Alonso, S., González-Prieto, Á.: Neural collaborative filtering classification model to obtain prediction reliabilities. Int. J. Interact. Multimedia Artif. Intell. (2021)
Bobadilla, J., Lara-Cabrera, R., González-Prieto, Á., Ortega, F.: Deepfair: deep learning for improving fairness in recommender systems (2020). arXiv preprint arXiv:2006.05255
Bobadilla, J., Ortega, F., Gutiérrez, A., Alonso, S.: Classification-based deep neural network architecture for collaborative filtering recommender systems. Int. J. Interact. Multimedia Artif. Intell. 6(1) (2020)
Çano, E., Morisio, M.: Hybrid recommender systems: a systematic literature review. Intell. Data Anal. 21(6), 1487–1524 (2017)
Deldjoo, Y., Schedl, M., Cremonesi, P., Pasi, G.: Recommender systems leveraging multimedia content. ACM Comput. Surv. (CSUR) 53(5), 1–38 (2020)
Févotte, C., Idier, J.: Algorithms for nonnegative matrix factorization with the \(\beta \)-divergence. Neural Comput. 23(9), 2421–2456 (2011)
Gao, M., Zhang, J., Yu, J., Li, J., Wen, J., Xiong, Q.: Recommender systems based on generative adversarial networks: a problem-driven perspective. Inform. Sci. 546, 1166–1185 (2021)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017)
Kulkarni, S., Rodd, S.F.: Context aware recommendation systems: a review of the state of the art techniques. Computer Sci. Rev. 37, 100255 (2020)
Narang, S., Taneja, N.: Deep content-collaborative recommender system (DCCRS). In: 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), pp. 110–116. IEEE (2018)
Ortega, F., Lara-Cabrera, R., González-Prieto, Á., Bobadilla, J.: Providing reliability in recommender systems through Bernoulli matrix factorization. Inform. Sci. 553, 110–128 (2021)
Ortega, F., Zhu, B., Bobadilla, J., Hernando, A.: Cf4j: collaborative filtering for java. Knowl. Based Syst. 152, 94–99 (2018)
Rendle, S., Krichene, W., Zhang, L., Anderson, J.: Neural collaborative filtering vs. matrix factorization revisited. In: Fourteenth ACM Conference on Recommender Systems, pp. 240–248 (2020)
Shokeen, J., Rana, C.: A study on features of social recommender systems. Artif. Intell. Rev. 53(2), 965–988 (2020)
Xue, H.J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209. Melbourne, Australia (2017)
Zhu, B., Hurtado, R., Bobadilla, J., Ortega, F.: An efficient recommender system method based on the numerical relevances and the non-numerical structures of the ratings. IEEE Access 6, 49935–49954 (2018)
Zhu, B., Ortega, F., Bobadilla, J., Gutiérrez, A.: Assigning reliability values to recommendations using matrix factorization. J. Comput. Sci. 26, 165–177 (2018)
Acknowledgements
This work was partially supported by Ministerio de Ciencia e Innovación of Spain under the project PID2019-106493RB-I00 (DL-CEMG) and the Comunidad de Madrid under Convenio Plurianual with the Universidad Politécnica de Madrid in the actuation line of Programa de Excelencia para el Profesorado Universitario.
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Bobadilla, J., Alonso, S., Gutiérrez, A., González, Á. (2022). Recommendation Versus Regression Neural Collaborative Filtering. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 309. Springer, Singapore. https://doi.org/10.1007/978-981-19-3444-5_2
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DOI: https://doi.org/10.1007/978-981-19-3444-5_2
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