Combination of individual and group patterns for time-sensitive purchase recommendation

Abstract

Due to the availability of large amounts of data, recommender systems have quickly gained popularity in the banking sphere. However, time-sensitive recommender systems, which take into account the temporal behavior and the recurrent activities of users to predict the expected time and category of next purchase, are still an active field of research. Many researchers tend to use population-level features or their low-rank approximations because the client’s purchase history is very sparse with few observations for some time intervals and product categories. But such approaches inevitably lead to a loss of accuracy. In this paper, we present a generative model of client spending based on the temporal point processes framework. The model is built in the way, to bring more individuality for the clients’ purchase behavior which takes into account individual purchase histories of clients. We also tackle the problem of poor statistics for people with a low transactional activity using effective intensity function parameterizations, and several other techniques such as smoothing daily intensity levels and taking into account population-level purchase rates for clients with a small number of transactions. The model is highly interpretable, and its training time scales linearly to millions of transactions and cubically to hundreds of thousands of users. Different temporal-process models were tested, and our model with all the incorporated modifications has shown the best results in terms of both error of time prediction and the accuracy of category prediction.

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References

  1. 1.

    Aalen, O., Borgan, O., Gjessing, H.: Survival and Event History Analysis: A Process Point of View. Springer Science & Business Media, Berlin (2008)

    Google Scholar 

  2. 2.

    Byrd, R.H., Lu, P., Nocedal, J., Zhu, C.: A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput. 16(5), 1190–1208 (1995)

    MathSciNet  Article  Google Scholar 

  3. 3.

    Dai, H., Wang, Y., Trivedi, R., Song, L.: Recurrent coevolutionary latent feature processes for continuous-time recommendation. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, ACM, pp 29–34 (2016)

  4. 4.

    Daley, D.J., Vere-Jones, D.: An introduction to the theory of point processes: volume II: general theory and structure. Springer Science & Business Media, Berlin (2007)

    Google Scholar 

  5. 5.

    Du, N., Dai, H., Trivedi, R., Upadhyay, U., Gomez-Rodriguez, M., Song, L.: Recurrent Marked Temporal Point Processes: Embedding Event History to Vector. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16 pp 1555–1564, https://doi.org/10.1145/2939672.2939875,http://dl.acm.org/citation.cfm?doid=2939672.2939875 (2016)

  6. 6.

    Grob, G.L., Cardoso, Â., Liu, C.H., Little, D.A., Chamberlain, B.P.: A recurrent neural network survival model: Predicting web user return time. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11053 LNAI:152–168, https://doi.org/10.1007/978-3-030-10997-4_10, arXiv:1807.04098v1 [cs.LG] (2019)

  7. 7.

    Koren, Y.: Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 447–456 (2009)

  8. 8.

    Kotzias, D., Lichman, M., Smyth, P.: Predicting consumption patterns with repeated and novel events. IEEE Trans. Knowl. Data Eng. 31(2), 371–384 (2018)

    Article  Google Scholar 

  9. 9.

    Lysenko, A., Shikov, E., Bochenina, K.: Temporal point processes for purchase categories forecasting. Proc. Computer Sci. 156, 255–263 (2019)

    Article  Google Scholar 

  10. 10.

    Manzoor, E., Akoglu, L.: Rush!: Targeted time-limited coupons via purchase forecasts. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp 1923–1931 (2017)

  11. 11.

    Mei, H., Eisner, J. M.: The neural hawkes process: A neurally self-modulating multivariate point process. In: Advances in Neural Information Processing Systems, pp 6754–6764 (2017)

  12. 12.

    Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th international conference on World wide web, ACM, pp 811–820 (2010)

  13. 13.

    Wang, S., Cao, L.: Inferring implicit rules by learning explicit and hidden item dependency. IEEE Transactions on Systems, Man, and Cybernetics: Systems (2017)

  14. 14.

    Wang, S., Hu, L., Wang, Y., Cao, L., Sheng, Q. Z., Orgun, M.: Sequential recommender systems: challenges, progress and prospects. arXiv preprint arXiv:2001.04830v1 [cs.IR] (2019a)

  15. 15.

    Wang, S., Hu, L., Wang, Y., Sheng, Q.Z., Orgun, M., Cao, L.: Modeling multi-purpose sessions for nextitem recommendations via mixture-channel purpose routing networks. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, AAAI Press, pp 1–7 (2019b)

  16. 16.

    Wang, Y., Du, N., Trivedi, R., Song, L.: Coevolutionary latent feature processes for continuous-time user-item interactions. In: Advances in Neural Information Processing Systems, pp 4547–4555 (2016)

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Acknowledgements

This research is financially supported by The Russian Science Foundation, Agreement NO19-71-10078.

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Correspondence to Anton Lysenko.

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A preliminary version of this work was presented at the Most-Rec Workshop at CIKM 2019, but it has not been published in any proceedings or journal before.

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Lysenko, A., Shikov, E. & Bochenina, K. Combination of individual and group patterns for time-sensitive purchase recommendation. Int J Data Sci Anal (2020). https://doi.org/10.1007/s41060-020-00233-1

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Keywords

  • Point processes
  • Transactional data
  • Mixture models
  • Recommendation
  • Machine learning