research-article

Modeling Within-Basket Auxiliary Item Recommendation with Matchability and Ubiquity

Authors:

Lina YaoAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology, Volume 14, Issue 3

Article No.: 49, Pages 1 - 19

https://doi.org/10.1145/3574157

Published: 13 April 2023 Publication History

Abstract

Within-basket recommendation is to recommend suitable items for the current basket with some already known items. The within-basket auxiliary item recommendation (WBAIR) is to recommend auxiliary items based on the primary items in the basket. Such a task exists in many real-life scenarios. Unlike the associations between items that can be transmitted in both directions, primary and auxiliary relationships are unidirectional. Then, the suitable matching patterns between primary and auxiliary items cannot be explored by traditional directionless methods. Therefore, we design the Matc4Rec algorithm to integrate the primary and auxiliary factors, and finally recommend items that not only match the interests of users but also satisfy the primary and auxiliary relationships between items. Specifically, we capture the pattern from three aspects: matchability within-basket, matchability between baskets, and ubiquity. By exploiting this pattern, the designed algorithm not only achieves good results on real-world datasets but also improves the interpretability of recommendations. As a result, we can know which commodities are suitable as auxiliary items. The experiment results demonstrate that our algorithm can also alleviate the cold start problem.

References

[1]

Jinze Bai, Chang Zhou, Junshuai Song, Xiaoru Qu, Weiting An, Zhao Li, and Jun Gao. 2019. Personalized bundle list recommendation. In Proceedings of the World Wide Web Conference. 60–71.

Digital Library

[2]

Oren Barkan and Noam Koenigstein. 2016. Item2vec: Neural item embedding for collaborative filtering. In Proceedings of the 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 1–6.

[3]

Moran Beladev, Lior Rokach, and Bracha Shapira. 2016. Recommender systems for product bundling. Knowledge-Based Systems 111 (2016), 193–206.

Digital Library

[4]

Da Cao, Xiangnan He, Lianhai Miao, Yahui An, Chao Yang, and Richang Hong. 2018. Attentive group recommendation. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. 645–654.

Digital Library

[5]

Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, et al. 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. 7–10.

Digital Library

[6]

Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014).

[7]

Yuxiao Dong, Nitesh V. Chawla, and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 135–144.

Digital Library

[8]

Mihajlo Grbovic, Vladan Radosavljevic, Nemanja Djuric, Narayan Bhamidipati, Jaikit Savla, Varun Bhagwan, and Doug Sharp. 2015. E-commerce in your inbox: Product recommendations at scale. In Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1809–1818.

Digital Library

[9]

Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 855–864.

Digital Library

[10]

Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: A factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017).

[11]

Xintong Han, Zuxuan Wu, Yu-Gang Jiang, and Larry S. Davis. 2017. Learning fashion compatibility with bidirectional lstms. In Proceedings of the 25th ACM International Conference on Multimedia. 1078–1086.

Digital Library

[12]

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).

[13]

Peter D. Hoff, Adrian E. Raftery, and Mark S. Handcock. 2002. Latent space approaches to social network analysis. Journal of the American Statistical Association 97, 460 (2002), 1090–1098.

[14]

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.

Digital Library

[15]

Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM’18). IEEE, Los Alamitos, CA, 197–206.

[16]

Farshad Kooti, Kristina Lerman, Luca Maria Aiello, Mihajlo Grbovic, Nemanja Djuric, and Vladan Radosavljevic. 2016. Portrait of an online shopper: Understanding and predicting consumer behavior. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining. 205–214.

Digital Library

[17]

Yehuda Koren. 2008. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 426–434.

Digital Library

[18]

Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural attentive session-based recommendation. In Proceedings of the 2017 ACM Conference on Information and Knowledge Management. 1419–1428.

Digital Library

[19]

Zhi Li, Hongke Zhao, Qi Liu, Zhenya Huang, Tao Mei, and Enhong Chen. 2018. Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1734–1743.

Digital Library

[20]

Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. 2018. XDeepFM: Combining explicit and implicit feature interactions for recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1754–1763.

Digital Library

[21]

Dawen Liang, Jaan Altosaar, Laurent Charlin, and David M. Blei. 2016. Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence. In Proceedings of the 10th ACM Conference on Recommender Systems. 59–66.

Digital Library

[22]

Guannan Liu, Yanjie Fu, Guoqing Chen, Hui Xiong, and Can Chen. 2017. Modeling buying motives for personalized product bundle recommendation. ACM Transactions on Knowledge Discovery from Data 11, 3 (2017), 1–26.

Digital Library

[23]

Aditya Krishna Menon and Charles Elkan. 2011. Link prediction via matrix factorization. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 437–452.

Digital Library

[24]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).

[25]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems. 3111–3119.

Digital Library

[26]

L’. Nagyová and I. Košičiarová. 2017. The convenience and bundling trends. In Consumer Trends and New Product Opportunities in the Food Sector, Klaus G. Grunert (Ed.). Wageningen Academic Publishers, 350–358.

[27]

Changhua Pei, Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, et al. 2019. Personalized re-ranking for recommendation. In Proceedings of the 13th ACM Conference on Recommender Systems. 3–11.

Digital Library

[28]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. DeepWalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 701–710.

Digital Library

[29]

Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. 2017. Personalizing session-based recommendations with hierarchical recurrent neural networks. In Proceedings of the 11th ACM Conference on Recommender Systems. 130–137.

Digital Library

[30]

Isabel Maria Rosa Diaz and Francisco Javier Rondán Cataluña. 2011. Antecedents of the importance of price in purchase decisions. Revista de Administração de Empresas 51 (2011), 370–381.

[31]

David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. 1985. Learning Internal Representations by Error Propagation. Technical Report. California University, San Diego, La Jolla Institute for Cognitive Science.

[32]

Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 565–573.

Digital Library

[33]

Lei Tang and Huan Liu. 2009. Relational learning via latent social dimensions. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 817–826.

Digital Library

[34]

Ilya Trofimov. 2018. Inferring complementary products from baskets and browsing sessions. arXiv preprint arXiv:1809.09621 (2018).

[35]

Trinh Xuan Tuan and Tu Minh Phuong. 2017. 3D convolutional networks for session-based recommendation with content features. In Proceedings of the 11th ACM Conference on Recommender Systems. 138–146.

Digital Library

[36]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems 30.

[37]

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.

Digital Library

[38]

Daixin Wang, Peng Cui, and Wenwu Zhu. 2016. Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1225–1234.

Digital Library

[39]

Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng. 2015. Learning hierarchical representation model for NextBasket recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 403–412.

Digital Library

[40]

Chen Wu and Ming Yan. 2017. Session-aware information embedding for e-commerce product recommendation. In Proceedings of the 2017 ACM Conference on Information and Knowledge Management. 2379–2382.

Digital Library

[41]

Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-based recommendation with graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 346–353.

Digital Library

[42]

En Xu, Zhiwen Yu, Bin Guo, and Helei Cui. 2021. Core interest network for click-through rate prediction. ACM Transactions on Knowledge Discovery from Data 15, 2 (2021), Article 23, 16 pages. DOI:

Digital Library

[43]

Xifeng Yan and Jiawei Han. 2002. gSpan: Graph-based substructure pattern mining. In Proceedings of the 2002 IEEE International Conference on Data Mining. IEEE, Los Alamitos, CA, 721–724.

[44]

Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 5941–5948.

Digital Library

[45]

Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1059–1068.

Digital Library

Cited By

Xu MChen XShe YJin YZhao GWang J(2024)Strengthening Cooperative Consensus in Multi-Robot ConfrontationACM Transactions on Intelligent Systems and Technology10.1145/363937115:2(1-27)Online publication date: 22-Feb-2024
https://dl.acm.org/doi/10.1145/3639371
Xu EZhao KYu ZZhang YGuo BYao L(2024)Limits of predictability in top-N recommendationInformation Processing & Management10.1016/j.ipm.2024.10373161:4(103731)Online publication date: Jul-2024
https://doi.org/10.1016/j.ipm.2024.103731
Xu YLi TYang YChen WYue L(2024)An adaptive category-aware recommender based on dual knowledge graphsInformation Processing & Management10.1016/j.ipm.2023.10363661:3(103636)Online publication date: May-2024
https://doi.org/10.1016/j.ipm.2023.103636
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Index Terms

Modeling Within-Basket Auxiliary Item Recommendation with Matchability and Ubiquity
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 14, Issue 3

June 2023

451 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3587032

Editor:
Huan Liu
Arizona State University, USA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 April 2023

Online AM: 17 February 2023

Accepted: 15 November 2022

Revised: 13 October 2022

Received: 04 January 2022

Published in TIST Volume 14, Issue 3

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Cited By

Xu MChen XShe YJin YZhao GWang J(2024)Strengthening Cooperative Consensus in Multi-Robot ConfrontationACM Transactions on Intelligent Systems and Technology10.1145/363937115:2(1-27)Online publication date: 22-Feb-2024
https://dl.acm.org/doi/10.1145/3639371
Xu EZhao KYu ZZhang YGuo BYao L(2024)Limits of predictability in top-N recommendationInformation Processing & Management10.1016/j.ipm.2024.10373161:4(103731)Online publication date: Jul-2024
https://doi.org/10.1016/j.ipm.2024.103731
Xu YLi TYang YChen WYue L(2024)An adaptive category-aware recommender based on dual knowledge graphsInformation Processing & Management10.1016/j.ipm.2023.10363661:3(103636)Online publication date: May-2024
https://doi.org/10.1016/j.ipm.2023.103636
Xu MShe YJin YWang J(2023)Dynamic Weights and Prior Reward in Policy Fusion for Compound Agent LearningACM Transactions on Intelligent Systems and Technology10.1145/362340514:6(1-28)Online publication date: 14-Nov-2023
https://dl.acm.org/doi/10.1145/3623405
Ye SLu J(undefined)Robust Recommender Systems with Rating Flip NoiseACM Transactions on Intelligent Systems and Technology10.1145/3641285
https://dl.acm.org/doi/10.1145/3641285

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