[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.21219/jitam.2018.25.2.073

Deep Neural Network Models to Recommend Product Repurchase at the Right Time : A Case Study for Grocery Stores

Song, Hee Seok (Department of Global IT Business in Hannam University)

Publication Information

Journal of Information Technology Applications and Management / v.25, no.2, 2018 , pp. 73-90 More about this Journal

Abstract

Despite of increasing studies for product recommendation, the recommendation of product repurchase timing has not yet been studied actively. This study aims to propose deep neural network models usingsimple purchase history data to predict the repurchase timing of each customer and compare performances of the models from the perspective of prediction quality, including expected ROI of promotion, variability of precision and recall, and diversity of target selection for promotion. As an experiment result, a recurrent neural network (RNN) model showed higher promotion ROI and the smaller variability compared to MLP and other models. The proposed model can be used to develop a CRM system that can offer SMS or app-based promotionsto the customer at the right time. This model can also be used to increase sales for product repurchase businesses by balancing the level of ordersas well as inducing repurchases by customers.

Keywords

Purchase Timing; Recommender; Multilayer Perceptron; Recurrent Neural Network; Retail Business; Product Repurchase; Promotion System;

Citations & Related Records

Reference

1	Yuan, Q., Cong, G., Ma, Z., Sun, A., and Magnenat-Thalmann, N., "Time-aware Point-of-interest Recommendation", Proceeding of SIGIR'13, 2013.
2	Zhao, G., Lee, M. L., and Wynne, H., "Utilizing Purchase Intervals in Latent Clusters for Product Recommendation", Proceedings of the 8th Workshop on Social Network Mining and Analysis (SNAKDD'14), 2014, pp. 1-9.
3	Zhu, Y., Li, H., Liao, Y., Wang, B., Guan, Z., Liu, H., and Cai, D., "What to Do Next : Modeling User Behaviors by Time-LSTM", Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017, pp. 3602-3608.
4	Wang, J. and Zhang, Y., "Opportunity Models for E-commerce Recommendation : Right Product, Right Time", Proceeding of SIGIR'13, 2013.
5	Bengio, Y., Simard, P., and Frasconi, P., "Learning long-term dependencies with gradient descent is difficult", IEEE Transactions on Neural Networks, Vol. 5, No. 2, 1994, pp. 157-166. DOI
6	Agrawal, R., Ieong, S., and Velu, R., "Timing When to Buy", ACM Conference on Information and Knowledge Management (CIKM), 2011.
7	Akermi, I., Boughanem, M., and Faiz, R., "Just-In-Time Recommendation Approach within a Mobile Context", IEEE/WIC/ACM International Conference on Web Intelligence, 2016, pp. 636-639.
8	Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., Bouchard, N., Warde-Farley, D., and Bengio, Y., "Theano : new features and speed improvements", NIPS 2012 deep learning workshop, 2012.
9	Chen, Y. L. and Huang, T. C. K., "Discovering fuzzy time-interval sequential patterns in sequence databases", IEEE Syst. Trans. Man Cybernet Part B, Vol. 35, No. 5, 2005, pp. 959-972. DOI
10	Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., WardeFarley, D., and Bengio, Y., "Theano : a CPU and GPU math expression compiler", Proceedings of the Python for Scientific Computing Conference (SciPy), 2010.
11	Chiang, D. A., Lee, S. L., Chen, C. C., and Wang, M. H., "Mining interval sequential patterns", International Journal of Intelligent System, Vol. 20, No. 3, 2005, pp. 359-373. DOI
12	Chung, J., Gulcehre, C., Cho, K., and Bengio, Y., "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling", NIPS 2014 Deep Learning and Representation Learning Workshop, 2014.
13	Deng, Z., Yan, M., Sang, J., and Xu, C., "Twitter is Faster : Personalized Time-Aware Video Recommendation from Twitter to YouTube", ACM Transactions on Multimedia Computing, Communications, and Applications, Vol. 11, No. 2, 2014.
14	Glorot, X. and Bengio, Y., "Understanding the difficulty of training deep feedforward neural networks", Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS'10), 2010.
15	Gould, B. W. and Dong, D., "The Decision of When to Buy a Frequently Purchased Good : A Multi-Period Probit Model", Journal of Agricultural and Resource Economics, Vol. 25, No. 2, 2000, pp. 636-652.
16	Hidasi, B., Karat-Zoglou, A., Baltrunas, L., and Tikk, D., "Session-based recommendations with recurrent neural networks", In ICLR, 2016.
17	Liu, S., Yang, N., Li, M., and Zhou, M., "A Recursive Recurrent Neural Network for Statistical Machine Translation", Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 2014, pp. 1491-1500.
18	Hinton, G. E., Osindero, S., and The, Y., "A fast learning, algorithm for deep belief nets", Neural Computation, Vol. 18, 2006, pp. 1527-1554. DOI
19	Hochreiter, S., "Long Short-Term Memory", Neural Computation, Vol. 9, No. 8, 1997, pp. 1735-1780. DOI
20	Hu, Y. H., Huang, T. C., Yang, H. R., and Chen, Y. L., "On mining multi-time-interval sequential patterns", Data Knowledge Engineering, Vol. 68, No. 10, 2009, pp. 1112-1127. DOI
21	Mulder, W. D., Bethard, S., and Moens, M. F., "A survey on the application of recurrent neural networks to statistical language modeling", Computer Speech & Language, Vol. 30, No. 1, 2015, pp. 61-98. DOI
22	Nielsen, A. M., Neural Networks and Deep Learning, Online ebook, 2015.
23	Oh, J., Kim, S., Kim, J., and Yu, H., "When to recommend : A new issue on TV show recommendation", Information Sciences, Vol. 280, No. 1, 2014, pp. 261-274. DOI
24	Oku, K., Nakajima, S., Miyazaki, J., Uemura, S., and Kato, H., "A Recommendation Method Considering Users' Time Series Contexts", Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication, 2009, pp. 465-470.
25	Rendle, S., Freudenthaler, C., and Schmidt-Thieme, L., "Factorizing personalized Markov chains for next-basket recommendation", WWW Conference, 2010, pp. 811-820.
26	Wang, J., Sarwar, B., and Sundaresan, N., "Utilizing related products for post-purchase recommendation in e-commerce", Proceedings of the fifth ACM conference on Recommender systems, 2011, pp. 329-332.
27	Sato, M., Izumo, H., and Sonoda, T., "Discount Sensitive Recommender System for Retail Business", Proceedings of the 3rd Workshop on Emotions and Personality in Personalized Systems, 2015, pp. 33-40.
28	Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R., "Dropout : A Simple Way to Prevent Neural Networks from Overfitting", Journal of Machine Learning Research, Vol. 15, 2014, pp. 1929-1958.
29	Tan, Y. K., Xu, X., and Liu, Y., "Improved recurrent neural networks for session-based recommendations", RecSys ACM, 2016, pp. 17-22.