Abstract
The aim of Recommender Systems is to suggest items (products) to satisfy each user’s particular taste. Representation strategies play a very important role in these systems, as an adequate codification of users and items is expected to ease the induction of a model which synthesizes their tastes and make better recommendations. However, in addition to gathering information about users’ tastes, there is an additional aspect that can be relevant for a proper codification strategy, namely the order in which the user interacted with the items. In this paper, several encoding strategies based on neural networks are analyzed and applied to solve two different recommendation tasks in the context of music playlists. The results show that the order in which the musical pieces were listened to is relevant for the codification of items (songs). We also find that the encoding of user profiles should use a different amount of historical data depending on the learning task to be solved. In other words, we do not always have to use all the available data; sometimes, it is better to discard old information, as tastes change over time.
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Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-scale machine learning on heterogeneous distributed systems (2016). arXiv:1603.04467
Caruana, R., Lawrence, S., Giles, C.L.: Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping. In: Leen, T.K., Dietterich, T.G., Tresp, V. (eds.) Advances in Neural Information Processing Systems, 13, pp. 402–408. MIT Press (2001)
Celma, O.: Music Recommendation and Discovery in the Long Tail. Springer, Berlin (2010)
Chan, N.N., Gaaloul, W., Tata, S.: A recommender system based on historical usage data for web service discovery. Serv. Oriented Comput. Appl. 6(1), 51–63 (2012)
Chen, S., Moore, J.L., Turnbull, D., Joachims, T.: Playlist prediction via metric embedding. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining, ACM, pp. 714–722 (2012)
Díez, J., Martínez-Rego, D., Alonso-Betanzos, A., Luaces, O., Bahamonde, A.: Optimizing novelty and diversity in recommendations. Prog. Artif. Intell. 8(1), 101–109 (2019)
Díez, J., Martínez-Rego, D., Alonso-Betanzos, A., Luaces, O., Bahamonde, A.: Metrical representation of readers and articles in a digital newspaper. In: 10th ACM Conference on Recommender Systems (RecSys 2016) Workshop on Profiling User Preferences for Dynamic Online and Real-Time Recommendations (RecProfile 2016). ACM (2016)
Fessahaye, F., Perez, L., Zhan, T., Zhang, R., Fossier, C., Markarian, R., Chiu, C., Zhan, J., Gewali, L., Oh, P.: T-recsys: a novel music recommendation system using deep learning. In: 2019 IEEE International Conference on Consumer Electronics (ICCE), IEEE, pp. 1–6 (2019)
Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 855–864 (2016)
Gutmann, M., Hyvärinen, A.: Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics. JMLR 13, 307–361 (2012)
Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: Proceedings of ACM Conference on Computer Supported Cooperative Work, ACM, pp. 241–250 (2000)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR) (2014)
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. CoRR abs/1405.4053 (2014)
Luaces, O., Díez, J., Joachims, T., Bahamonde, A.: Mapping preferences into euclidean space. Expert Syst. Appl. 42(22), 8588–8596 (2015)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013)
Pazzani, M.J., Billsus, D.: The Adaptive Web Chap Content-based recommendation systems, pp. 325–341. Springer, Berlin (2007)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 701–710 (2014)
Pérez-Núñez, P., Luaces, O., Bahamonde, A., Díez, J.: Representaciones basadas en redes neuronales para tareas de recomendación. In: Proceedings of CAEPIA 2018, AEPIA, pp. 1179–1184 (2018)
Qiu, J., Dong, Y., Ma, H., Li, J., Wang, K., Tang, J.: Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, ACM, pp. 459–467 (2018)
Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997). https://doi.org/10.1145/245108.245121
Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Boston (2011)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077. International World Wide Web Conferences Steering Committee (2015)
Weston, J., Bengio, S., Hamel, P.: Multi-tasking with joint semantic spaces for large-scale music annotation and retrieval. J. New Music Res. 40(4), 337–348 (2011)
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We are grateful to NVIDIA Corporation for the donation of the Titan Xp GPU used in this research.
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This work was funded under Grants TIN2015-65069-C2-2-R from the MINECO (Spanish Ministry of the Economy and Competitiveness) and IDI-2018-000176 from the Principado de Asturias Regional Government, partially supported with ERDF funds.
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Pérez-Núñez, P., Luaces, O., Bahamonde, A. et al. Improving recommender systems by encoding items and user profiles considering the order in their consumption history. Prog Artif Intell 9, 67–75 (2020). https://doi.org/10.1007/s13748-019-00199-7
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DOI: https://doi.org/10.1007/s13748-019-00199-7