research-article

Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors

Authors:

Enhong ChenAuthors Info & Claims

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 1734 - 1743

https://doi.org/10.1145/3219819.3220014

Published: 19 July 2018 Publication History

Abstract

In the modern e-commerce, the behaviors of customers contain rich information, e.g., consumption habits, the dynamics of preferences. Recently, session-based recommendationsare becoming popular to explore the temporal characteristics of customers' interactive behaviors. However, existing works mainly exploit the short-term behaviors without fully taking the customers' long-term stable preferences and evolutions into account. In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users' historical stable preferences and present consumption motivations. Specifically, BINN contains two main components, i.e., Neural Item Embedding, and Discriminative Behaviors Learning. Firstly, a novel item embedding method based on user interactions is developed for obtaining an unified representation for each item. Then, with the embedded items and the interactive behaviors over item sequences, BINN discriminatively learns the historical preferences and present motivations of the target users. Thus, BINN could better perform recommendations of the next items for the target users. Finally, for evaluating the performances of BINN, we conduct extensive experiments on two real-world datasets, i.e., Tianchi and JD. The experimental results clearly demonstrate the effectiveness of BINN compared with several state-of-the-art methods.

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Cited By

Xiao JPan WMing ZHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)A Generic Behavior-Aware Data Augmentation Framework for Sequential RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657682(1578-1588)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657682
Li XChen HYu JZhao MXu TZhang WYu MAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Global Heterogeneous Graph and Target Interest Denoising for Multi-behavior Sequential RecommendationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635857(387-395)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635857
Zheng XWu RHan ZChen CChen LHan B(2024)Heterogeneous Information Crossing on Graphs for Session-Based Recommender SystemsACM Transactions on the Web10.1145/357240718:2(1-24)Online publication date: 8-Jan-2024
https://dl.acm.org/doi/10.1145/3572407
Show More Cited By

Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications

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KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2018

2925 pages

ISBN:9781450355520

DOI:10.1145/3219819

General Chairs:
Yike Guo
Imperial College London
,
Faisal Farooq
IBM

Copyright © 2018 ACM.

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Published: 19 July 2018

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National Natural Science Foundation of China
Young Elite Scientist Sponsorship Program of CAST and the Youth Innovation Promotion Association of CAS
National Key Research and Development Program of China

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KDD '18

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KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 19 - 23, 2018

London, United Kingdom

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KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Xiao JPan WMing ZHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)A Generic Behavior-Aware Data Augmentation Framework for Sequential RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657682(1578-1588)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657682
Li XChen HYu JZhao MXu TZhang WYu MAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Global Heterogeneous Graph and Target Interest Denoising for Multi-behavior Sequential RecommendationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635857(387-395)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635857
Zheng XWu RHan ZChen CChen LHan B(2024)Heterogeneous Information Crossing on Graphs for Session-Based Recommender SystemsACM Transactions on the Web10.1145/357240718:2(1-24)Online publication date: 8-Jan-2024
https://dl.acm.org/doi/10.1145/3572407
Qin YJu WWu HLuo XZhang M(2024)Learning Graph ODE for Continuous-Time Sequential RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.3349397(1-14)Online publication date: 2024
https://doi.org/10.1109/TKDE.2024.3349397
Zheng XLi XChen ZSun LYu QGuo LLuo Y(2024)Enhanced Self-Attention Mechanism for Long and Short Term Sequential Recommendation ModelsIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33667718:3(2457-2466)Online publication date: Jul-2024
https://doi.org/10.1109/TETCI.2024.3366771
Chen YChen YHsu C(2024)G-TransRec: A Transformer-Based Next-Item Recommendation With Time PredictionIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.335431511:3(4175-4188)Online publication date: Jul-2024
https://doi.org/10.1109/TCSS.2024.3354315
Wu BCheng YYuan HMa Q(2024)When Multi-Behavior Meets Multi-Interest: Multi-Behavior Sequential Recommendation with Multi-Interest Self-Supervised Learning2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00070(845-858)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00070
Chen QJiang FGuo XChen JSha KWang Y(2024)Combine temporal information in session-based recommendation with graph neural networksExpert Systems with Applications10.1016/j.eswa.2023.121969238(121969)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121969
Nahta RChauhan GMeena YGopalani D(2024)Deep learning with the generative models for recommender systems: A surveyComputer Science Review10.1016/j.cosrev.2024.10064653(100646)Online publication date: Aug-2024
https://doi.org/10.1016/j.cosrev.2024.100646
Li WWu YLiu YPan WMing Z(2024)BMLP: behavior-aware MLP for heterogeneous sequential recommendationFrontiers of Computer Science10.1007/s11704-023-2703-y18:3Online publication date: 13-Jan-2024
https://doi.org/10.1007/s11704-023-2703-y
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