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Flexible Order Aware Sequential Recommendation

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Bo LiAuthors Info & Claims

ICMR '22: Proceedings of the 2022 International Conference on Multimedia Retrieval

Pages 109 - 117

https://doi.org/10.1145/3512527.3531407

Published: 27 June 2022 Publication History

Abstract

Sequential recommendations can dynamically model user interests, which has great value since users' interests may change rapidly with time. Traditional sequential recommendation methods assume that the user behaviors are rigidly ordered and sequentially dependent. However, some user behaviors have flexible orders, meaning the behaviors may occur in any order and are not sequentially dependent. Therefore, traditional methods may capture inaccurate user interests based on wrong dependencies. Motivated by this, several methods identify flexible orders by continuity or similarity. However, these methods fail to comprehensively understand the nature of flexible orders since continuity or similarity do not determine order flexibilities. Therefore, these methods may misidentify flexible orders, leading to inappropriate recommendations. To address these issues, we propose a Flexible Order aware Sequential Recommendation (FOSR) method to identify flexible orders comprehensively. We argue that orders' flexibilities are highly related to the frequencies of item pair co-occurrences. In light of this, FOSR employs a probabilistic based flexible order evaluation module to simulate item pair frequencies and infer accurate order flexibilities. The frequency labeling module extracts labels from the real item pair frequencies to guide the order flexibility measurement. Given the measured order flexibilities, we develop a flexible order aware self-attention module to model dependencies from flexible orders comprehensively and learn dynamic user interests effectively. Extensive experiments on four benchmark datasets show that our model outperforms various state-of-the-art sequential recommendation methods.

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Cited By

Jiang YXu YYang YYang FWang PLi CZhuang FXiong H(2024)TriMLP: A Foundational MLP-Like Architecture for Sequential RecommendationACM Transactions on Information Systems10.1145/367099542:6(1-34)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3670995
Li MAriannezhad MYates Ade Rijke M(2023)Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery ShoppingProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608803(35-46)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608803
Li MVardasbi AYates Ade Rijke MChen HDuh WHuang HKato MMothe JPoblete B(2023)Repetition and Exploration in Sequential RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591914(2532-2541)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591914
Show More Cited By

Index Terms

Flexible Order Aware Sequential Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

ICMR '22: Proceedings of the 2022 International Conference on Multimedia Retrieval

June 2022

714 pages

ISBN:9781450392389

DOI:10.1145/3512527

General Chairs:
Vincent Oria
New Jersey Institute of Technology, USA
,
Maria Luisa Sapino
Università degli Studi di Torino, Italy
,
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Brigitte Kerhervé
Université du Québec à Montréal, Canada
,
Program Chairs:
Wen-Huang Cheng
National Yang Ming Chao Tung University, Taiwan
,
Ichiro Ide
Nagoya University, Japan
,
Vivek Singh
Rutgers University, USA

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 June 2022

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ICMR '22

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SIGMM

ICMR '22: International Conference on Multimedia Retrieval

June 27 - 30, 2022

NJ, Newark, USA

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Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Jiang YXu YYang YYang FWang PLi CZhuang FXiong H(2024)TriMLP: A Foundational MLP-Like Architecture for Sequential RecommendationACM Transactions on Information Systems10.1145/367099542:6(1-34)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3670995
Li MAriannezhad MYates Ade Rijke M(2023)Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery ShoppingProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608803(35-46)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608803
Li MVardasbi AYates Ade Rijke MChen HDuh WHuang HKato MMothe JPoblete B(2023)Repetition and Exploration in Sequential RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591914(2532-2541)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591914
Qian MDai FGu XFan HLiu DLi B(2023)User Popularity Preference Aware Sequential RecommendationComputational Science – ICCS 202310.1007/978-3-031-35995-8_8(104-118)Online publication date: 3-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-35995-8_8

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