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Learning Hierarchical Representation Model for NextBasket Recommendation

Published: 09 August 2015 Publication History

Abstract

Next basket recommendation is a crucial task in market basket analysis. Given a user's purchase history, usually a sequence of transaction data, one attempts to build a recommender that can predict the next few items that the user most probably would like. Ideally, a good recommender should be able to explore the sequential behavior (i.e., buying one item leads to buying another next), as well as account for users' general taste (i.e., what items a user is typically interested in) for recommendation. Moreover, these two factors may interact with each other to influence users' next purchase. To tackle the above problems, in this paper, we introduce a novel recommendation approach, namely hierarchical representation model (HRM). HRM can well capture both sequential behavior and users' general taste by involving transaction and user representations in prediction. Meanwhile, the flexibility of applying different aggregation operations, especially nonlinear operations, on representations allows us to model complicated interactions among different factors. Theoretically, we show that our model subsumes several existing methods when choosing proper aggregation operations. Empirically, we demonstrate that our model can consistently outperform the state-of-the-art baselines under different evaluation metrics on real-world transaction data.

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    cover image ACM Conferences
    SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval
    August 2015
    1198 pages
    ISBN:9781450336215
    DOI:10.1145/2766462
    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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    Published: 09 August 2015

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    Author Tags

    1. general taste
    2. hierarchical representation model
    3. next basket recommendation
    4. sequential behavior

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    • Research-article

    Funding Sources

    • 973 Program of China
    • 863 Program of China
    • Project supported by the National Natural Science Foundation of China

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    SIGIR '15
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    SIGIR '15 Paper Acceptance Rate 70 of 351 submissions, 20%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    • (2025)Graphical contrastive learning for multi-interest sequential recommendationExpert Systems with Applications10.1016/j.eswa.2024.125285259(125285)Online publication date: Jan-2025
    • (2024)Sequential Recommendation Model Based on Deep Residual Recurrent Neural NetworkScientific Insights and Discoveries Review10.59782/sidr.v2i1.572:1(27-35)Online publication date: 7-Oct-2024
    • (2024)MSD: Multi-Order Semantic Denoising Model for Session-Based RecommendationsElectronics10.3390/electronics1316311813:16(3118)Online publication date: 7-Aug-2024
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