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Novel next-group recommendation approach based on sequential market basket information

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  • Li-Ching Ma

    (National United University)

Abstract

A market basket is a set of items included in a retail assortment that a customer buys on a shopping trip. The purpose of market basket analysis is to persuade a customer to spend more money through upselling or cross-selling. Most recommendation systems only suggest a single next-item or the top n items that a customer is most likely to buy. A company might succeed in convincing a customer to spend more money to increase sales revenue if a recommendation system can suggest the next or top n groups of items that customers are likely to buy according to the items in their basket. Based on the similarity upper approximation clustering, Borda majority count and PrefixSpan algorithm, this paper proposes a novel next-group recommendation approach according to sequential market basket information. Compared with the previous methods, the proposed approach can provide next-group instead of next-item recommendation, which may create more opportunities for customers to increase their spending.

Suggested Citation

  • Li-Ching Ma, 2023. "Novel next-group recommendation approach based on sequential market basket information," Electronic Commerce Research, Springer, vol. 23(4), pages 2399-2418, December.
    • Handle: RePEc:spr:elcore:v:23:y:2023:i:4:d:10.1007_s10660-022-09543-x
    DOI: 10.1007/s10660-022-09543-x
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    References listed on IDEAS

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    1. Boztug, Yasemin & Reutterer, Thomas, 2008. "A combined approach for segment-specific market basket analysis," European Journal of Operational Research, Elsevier, vol. 187(1), pages 294-312, May.
    2. Li-Ching Ma, 2018. "Discovering Consensus Preferences Visually Based on Gower Plots," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 741-761, May.
    3. Nan Jing & Tao Jiang & Juan Du & Vijayan Sugumaran, 2018. "Personalized recommendation based on customer preference mining and sentiment assessment from a Chinese e-commerce website," Electronic Commerce Research, Springer, vol. 18(1), pages 159-179, March.
    4. Chen, Yen-Liang & Cheng, Li-Chen, 2009. "Mining maximum consensus sequences from group ranking data," European Journal of Operational Research, Elsevier, vol. 198(1), pages 241-251, October.
    5. Pradeep Kumar & Bapi S. Raju & P. Radha Krishna, 2010. "A New Similarity Metric for Sequential Data," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 6(4), pages 16-32, October.
    6. Ma, Li-Ching, 2016. "A new group ranking approach for ordinal preferences based on group maximum consensus sequences," European Journal of Operational Research, Elsevier, vol. 251(1), pages 171-181.
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