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.

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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
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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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