research-article

CTRec: A Long-Short Demands Evolution Model for Continuous-Time Recommendation

Authors:

Wayne Xin Zhao,

Ji-Rong WenAuthors Info & Claims

SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 675 - 684

https://doi.org/10.1145/3331184.3331199

Published: 18 July 2019 Publication History

Abstract

In e-commerce, users' demands are not only conditioned by their profile and preferences, but also by their recent purchases that may generate new demands, as well as periodical demands that depend on purchases made some time ago. We call them respectively short-term demands and long-term demands. In this paper, we propose a novel self-attentive Continuous-Time Recommendation model (CTRec) for capturing the evolving demands of users over time. For modeling such time-sensitive demands, a Demand-aware Hawkes Process (DHP) framework is designed in CTRec to learn from the discrete purchase records of users. More specifically, a convolutional neural network is utilized to capture the short-term demands; and a self-attention mechanism is employed to capture the periodical purchase cycles of long-term demands. All types of demands are fused in DHP to make final continuous-time recommendations. We conduct extensive experiments on four real-world commercial datasets to demonstrate that CTRec is effective for general sequential recommendation problems, including next-item and next-session/basket recommendations. We observe in particular that CTRec is capable of learning the purchase cycles of products and estimating the purchase time of a product given a user.

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Cited By

Lan WZhou GChen QWang WPan SPan YZhang S(2024)Contrastive Clustering Learning for Multi-Behavior RecommendationACM Transactions on Information Systems10.1145/369819243:1(1-23)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1145/3698192
Katz OBarkan OKoenigstein N(2024)Personalized Cadence Awareness for Next Basket RecommendationACM Transactions on Recommender Systems10.1145/36528633:1(1-23)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3652863
Ma CRen YCastells PSanderson MChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Temporal Conformity-aware Hawkes Graph Network for RecommendationsProceedings of the ACM Web Conference 202410.1145/3589334.3645354(3185-3194)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645354
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Index Terms

CTRec: A Long-Short Demands Evolution Model for Continuous-Time Recommendation
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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cover image ACM Conferences

SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2019

1512 pages

ISBN:9781450361729

DOI:10.1145/3331184

General Chairs:
Benjamin Piwowarski
CNRS - Sorbonne Universite, France
,
Max Chevalier
Universite de Toulouse, CNRS, France
,
Eric Gaussier
Universite Grenoble Alpes, CNRS, France
,
Program Chairs:
Yoelle Maarek
Amazon Research, Israel
,
Jian-Yun Nie
University of Montreal, Canada
,
Falk Scholer
RMIT University, Australia

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Published: 18 July 2019

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National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Research Funds of Renmin University of China

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SIGIR '19

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SIGIR

SIGIR '19: The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 21 - 25, 2019

Paris, France

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SIGIR'19 Paper Acceptance Rate 84 of 426 submissions, 20%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Lan WZhou GChen QWang WPan SPan YZhang S(2024)Contrastive Clustering Learning for Multi-Behavior RecommendationACM Transactions on Information Systems10.1145/369819243:1(1-23)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1145/3698192
Katz OBarkan OKoenigstein N(2024)Personalized Cadence Awareness for Next Basket RecommendationACM Transactions on Recommender Systems10.1145/36528633:1(1-23)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3652863
Ma CRen YCastells PSanderson MChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Temporal Conformity-aware Hawkes Graph Network for RecommendationsProceedings of the ACM Web Conference 202410.1145/3589334.3645354(3185-3194)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645354
Guo LZhang JTang LChen TZhu LYin H(2024)Time Interval-Enhanced Graph Neural Network for Shared-Account Cross-Domain Sequential RecommendationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.320153335:3(4002-4016)Online publication date: Mar-2024
https://doi.org/10.1109/TNNLS.2022.3201533
Wu WGhazali MHazlin Huspi S(2024)A Review of User Profiling Based on Social NetworksIEEE Access10.1109/ACCESS.2024.343098712(122642-122670)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3430987
Jansen LBennin Kvan Kleef EVan Loo E(2024)Online grocery shopping recommender systemsComputers in Human Behavior10.1016/j.chb.2024.108336159:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.chb.2024.108336
Zhang KChu DTu ZLiu XZhang B(2024)LSTM-UBI: a user behavior inertia based recommendation methodMultimedia Tools and Applications10.1007/s11042-024-18256-283:27(69227-69248)Online publication date: 31-Jan-2024
https://doi.org/10.1007/s11042-024-18256-2
Koh JChen C(2024)Category-Aware Sequential Recommendation with Time Intervals of PurchasesDatabase and Expert Systems Applications10.1007/978-3-031-68309-1_21(249-257)Online publication date: 18-Aug-2024
https://doi.org/10.1007/978-3-031-68309-1_21
Zheng LChai HChen XJin JZhang WYu YGuo XGe CFeng Z(2023)Search-based Time-Aware Graph-Enhanced Recommendation with Sequential Behavior DataACM Transactions on Recommender Systems10.1145/3605356Online publication date: 27-Jun-2023
https://doi.org/10.1145/3605356
Ding HKveton BMa YPark YKini VGu YDivvela RWang FDeoras AWang H(2023)Trending Now: Modeling Trend RecommendationsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608810(294-305)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608810
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