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Modeling Buying Motives for Personalized Product Bundle Recommendation

Published: 06 March 2017 Publication History

Abstract

Product bundling is a marketing strategy that offers several products/items for sale as one bundle. While the bundling strategy has been widely used, less efforts have been made to understand how items should be bundled with respect to consumers’ preferences and buying motives for product bundles. This article investigates the relationships between the items that are bought together within a product bundle. To that end, each purchased product bundle is formulated as a bundle graph with items as nodes and the associations between pairs of items in the bundle as edges. The relationships between items can be analyzed by the formation of edges in bundle graphs, which can be attributed to the associations of feature aspects. Then, a probabilistic model BPM (Bundle Purchases with Motives) is proposed to capture the composition of each bundle graph, with two latent factors node-type and edge-type introduced to describe the feature aspects and relationships respectively. Furthermore, based on the preferences inferred from the model, an approach for recommending items to form product bundles is developed by estimating the probability that a consumer would buy an associative item together with the item already bought in the shopping cart. Finally, experimental results on real-world transaction data collected from well-known shopping sites show the effectiveness advantages of the proposed approach over other baseline methods. Moreover, the experiments also show that the proposed model can explain consumers’ buying motives for product bundles in terms of different node-types and edge-types.

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    Published In

    cover image ACM Transactions on Knowledge Discovery from Data
    ACM Transactions on Knowledge Discovery from Data  Volume 11, Issue 3
    August 2017
    372 pages
    ISSN:1556-4681
    EISSN:1556-472X
    DOI:10.1145/3058790
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 06 March 2017
    Accepted: 01 November 2016
    Revised: 01 September 2016
    Received: 01 November 2015
    Published in TKDD Volume 11, Issue 3

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    Author Tags

    1. Product bundle
    2. buying motives
    3. probabilistic graphical model
    4. recommendation

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    Funding Sources

    • National Natural Science Foundation of China
    • MOE Project of Key Research Institute of Humanities and Social Sciences at Universities

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    • (2024)Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for RecommendationsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672056(944-955)Online publication date: 25-Aug-2024
    • (2024)Towards Hierarchical Intent Disentanglement for Bundle RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3329175(1-12)Online publication date: 2024
    • (2024)Simultaneous consideration of consumer preferences and seller revenue as a smart retail sales and management strategyEuropean Journal of Management and Business Economics10.1108/EJMBE-04-2022-0105Online publication date: 9-Jan-2024
    • (2024)Outlier item detection in bundle recommendation via the attention mechanismHigh-Confidence Computing10.1016/j.hcc.2024.1002004:3(100200)Online publication date: Sep-2024
    • (2024)Multi-view denoising contrastive learning for bundle recommendationApplied Intelligence10.1007/s10489-024-05825-z54:23(12332-12346)Online publication date: 1-Dec-2024
    • (2023)Simplifying Learning Experience on a Personalized Content Recommendation System for Complex Text Material in E-LearningAdvanced Applications of Generative AI and Natural Language Processing Models10.4018/979-8-3693-0502-7.ch006(108-123)Online publication date: 29-Dec-2023
    • (2023)Modeling Within-Basket Auxiliary Item Recommendation with Matchability and UbiquityACM Transactions on Intelligent Systems and Technology10.1145/3574157Online publication date: 17-Feb-2023
    • (2023)Learning Distinct Relationship in Package Recommendation With Graph Attention NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.321056710:6(3308-3320)Online publication date: Dec-2023
    • (2023)A Hybrid Laptop Recommendation System for Engineering Undergraduates2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES)10.1109/CISES58720.2023.10183587(967-973)Online publication date: 28-Apr-2023
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