Ori Katz
ABSTRACT
The problem of Next Basket Recommendation (NBR) addresses the challenge of recommending items for the next basket of a user, based on her sequence of prior baskets. In this paper, we focus on a variation of this problem in which we aim to predict repurchases, i.e. we wish to recommend a user only items she had purchased before. We coin this problem Next Basket Repurchase Recommendation (NBRR). Over the years, a variety of models have been proposed to address the problem of NBR, however, the problem of NBRR has been overlooked. Although being highly related problems, which are often solved by the same methods, the problem of repurchase recommendation calls for a different approach. In this paper, we share insights from our experience of facing the challenge of NBRR. In light of these insights, we propose a novel hyper-convolutional model to leverage the behavioral patterns of repeated purchases. We demonstrate the effectiveness of the proposed model on three publicly available datasets, where it is shown to outperform other existing methods across multiple metrics.
- Ashton Anderson, Ravi Kumar, Andrew Tomkins, and Sergei Vassilvitskii. 2014. The dynamics of repeat consumption. In Proceedings of the 23rd international conference on World wide web. 419–430.Google Scholar
Digital Library
- Mozhdeh Ariannezhad, Sami Jullien, Ming Li, Min Fang, Sebastian Schelter, and Maarten de Rijke. 2022. ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping. (2022).Google Scholar
- Ting Bai, Jian-Yun Nie, Wayne Xin Zhao, Yutao Zhu, Pan Du, and Ji-Rong Wen. 2018. An Attribute-Aware Neural Attentive Model for Next Basket Recommendation. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval(Ann Arbor, MI, USA) (SIGIR ’18). Association for Computing Machinery, New York, NY, USA, 1201–1204. https://doi.org/10.1145/3209978.3210129Google ScholarDigital Library
- Ting Bai, Lixin Zou, Wayne Xin Zhao, Pan Du, Weidong Liu, Jian-Yun Nie, and Ji-Rong Wen. 2019. Ctrec: A long-short demands evolution model for continuous-time recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 675–684.Google ScholarDigital Library
- Pengda Chen and Jian Li. 2019. A recurrent model with self-attention for product repurchase recommendation. In Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence. 199–203.Google ScholarDigital Library
- Pengda Chen and Jian Li. 2019. Weighted sequence loss based recurrent model for repurchase recommendation. In IOP Conference Series: Materials Science and Engineering, Vol. 490. IOP Publishing, 062062.Google ScholarCross Ref
- John Dawes and Magda Nenycz-Thiel. 2014. Comparing retailer purchase patterns and brand metrics for in-store and online grocery purchasing. Journal of Marketing Management 30, 3-4 (2014), 364–382.Google ScholarCross Ref
- DSSResources. 2002. What is the ”true story” about data mining, beer and diapers. http://www.dssresources.com/newsletters/66.phpGoogle Scholar
- Dunnhumby. 2017. The Complete Journey. https://www.dunnhumby.com/source-files/Google Scholar
- Guglielmo Faggioli, Mirko Polato, and Fabio Aiolli. 2020. Recency aware collaborative filtering for next basket recommendation. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. 80–87.Google ScholarDigital Library
- Miroslav Fiedler. 1973. Algebraic connectivity of graphs. Czechoslovak mathematical journal 23, 2 (1973), 298–305.Google Scholar
- Savi Gupta and Roopal Mamtora. 2014. A survey on association rule mining in market basket analysis. International Journal of Information and Computation Technology 4, 4(2014), 409–414.Google Scholar
- David Ha, Andrew M. Dai, and Quoc V. Le. 2016. HyperNetworks. CoRR abs/1609.09106(2016). http://arxiv.org/abs/1609.09106Google Scholar
- Torben Hansen. 2008. Consumer values, the theory of planned behaviour and online grocery shopping. International Journal of Consumer Studies 32, 2 (2008), 128–137.Google ScholarCross Ref
- Haoji Hu, Xiangnan He, Jinyang Gao, and Zhi-Li Zhang. 2020. Modeling personalized item frequency information for next-basket recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1071–1080.Google ScholarDigital Library
- Instacart. 2017. The Instacart Online Grocery Shopping Dataset 2017. https://www.instacart.com/datasets/grocery-shopping-2017Google Scholar
- Kaggle. 2013. Acquire Valued Shoppers. https://www.kaggle.com/c/acquire-valued-shoppers-challenge/overviewGoogle Scholar
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014).Google Scholar
- Benjamin Klein, Lior Wolf, and Yehuda Afek. 2015. A dynamic convolutional layer for short range weather prediction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4840–4848.Google Scholar
- Mathias Kraus and Stefan Feuerriegel. 2019. Personalized purchase prediction of market baskets with Wasserstein-based sequence matching. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2643–2652.Google ScholarDigital Library
- Fachrul Kurniawan, Binti Umayah, Jihad Hammad, Supeno Mardi Susiki Nugroho, and Mochammad Hariadi. 2018. Market Basket Analysis to identify customer behaviours by way of transaction data. Knowledge Engineering and Data Science 1, 1 (2018), 20.Google ScholarCross Ref
- Duc-Trong Le, Hady W Lauw, and Yuan Fang. 2019. Correlation-sensitive next-basket recommendation. (2019).Google Scholar
- Lukas Lerche, Dietmar Jannach, and Malte Ludewig. 2016. On the value of reminders within e-commerce recommendations. In Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization. 27–35.Google ScholarDigital Library
- Ming Li, Benjamin M Dias, Ian Jarman, Wael El-Deredy, and Paulo JG Lisboa. 2009. Grocery shopping recommendations based on basket-sensitive random walk. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. 1215–1224.Google ScholarDigital Library
- Ming Li, Sami Jullien, Mozhdeh Ariannezhad, and Maarten de Rijke. 2021. A Next Basket Recommendation Reality Check. arXiv preprint arXiv:2109.14233(2021).Google Scholar
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781(2013).Google Scholar
- Warnia Nengsih. 2015. A comparative study on market basket analysis and apriori association technique. In 2015 3rd international conference on information and communication technology (ICoICT). IEEE, 461–464.Google ScholarCross Ref
- Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing Personalized Markov Chains for Next-Basket Recommendation. In Proceedings of the 19th International Conference on World Wide Web (Raleigh, North Carolina, USA) (WWW ’10). Association for Computing Machinery, New York, NY, USA, 811–820. https://doi.org/10.1145/1772690.1772773Google ScholarDigital Library
- Mengting Wan, Di Wang, Jie Liu, Paul Bennett, and Julian McAuley. 2018. Representing and recommending shopping baskets with complementarity, compatibility and loyalty. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 1133–1142.Google ScholarDigital Library
- Shoujin Wang, Liang Hu, Longbing Cao, Xiaoshui Huang, Defu Lian, and Wei Liu. 2018. Attention-based transactional context embedding for next-item recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.Google ScholarCross Ref
- Shoujin Wang, Liang Hu, Yan Wang, Quan Z Sheng, Mehmet Orgun, and Longbing Cao. 2019. Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks. In International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence.Google ScholarCross Ref
- Felix Wu, Angela Fan, Alexei Baevski, Yann N Dauphin, and Michael Auli. 2019. Pay less attention with lightweight and dynamic convolutions. arXiv preprint arXiv:1901.10430(2019).Google Scholar
- Feng Yu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. A Dynamic Recurrent Model for Next Basket Recommendation. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (Pisa, Italy) (SIGIR ’16). Association for Computing Machinery, New York, NY, USA, 729–732. https://doi.org/10.1145/2911451.2914683Google ScholarDigital Library
- Mindi Yuan, Yannis Pavlidis, Mukesh Jain, and Kristy Caster. 2016. Walmart online grocery personalization: Behavioral insights and basket recommendations. In International Conference on Conceptual Modeling. Springer, 49–64.Google ScholarCross Ref
Index Terms
- Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Recommendation
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