research-article

Modeling Multiple Coexisting Category-Level Intentions for Next Item Recommendation

Authors:

Jiadi YuAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 39, Issue 3

Article No.: 23, Pages 1 - 24

https://doi.org/10.1145/3441642

Published: 05 May 2021 Publication History

Abstract

Purchase intentions have a great impact on future purchases and thus can be exploited for making recommendations. However, purchase intentions are typically complex and may change from time to time. Through empirical study with two e-commerce datasets, we observe that behaviors of multiple types can indicate user intentions and a user may have multiple coexisting category-level intentions that evolve over time. In this article, we propose a novel Intention-Aware Recommender System (IARS) which consists of four components for mining such complex intentions from user behaviors of multiple types. In the first component, we utilize several Recurrent Neural Networks (RNNs) and an attention layer to model diverse user intentions simultaneously and design two kinds of Multi-behavior GRU (MGRU) cells to deal with heterogeneous behaviors. To reveal user intentions, we carefully design three tasks that share representations from MGRUs. The next-item recommendation is the main task and leverages attention to select user intentions according to candidate items. The remaining two (item prediction and sequence comparison) are auxiliary tasks and can reveal user intentions. Extensive experiments on the two real-world datasets demonstrate the effectiveness of our models compared with several state-of-the-art recommendation methods in terms of hit ratio and NDCG.

References

[1]

Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 8 (2013), 1798–1828.

Digital Library

[2]

Yoshua Bengio and Olivier Delalleau. 2011. On the expressive power of deep architectures. In International Conference on Algorithmic Learning Theory. 18–36.

[3]

Hung-Hsuan Chen. 2018. Behavior2Vec: Generating distributed representations of users’ behaviors on products for recommender systems. TKDD 12, 4 (2018), 43.

Digital Library

[4]

Tong Chen, Hongzhi Yin, Hongxu Chen, Rui Yan, Quoc Viet Hung Nguyen, and Xue Li. 2019. AIR: Attentional intention-aware recommender systems. In ICDE. 304–315.

[5]

Xinchi Chen, Zhan Shi, Xipeng Qiu, and Xuanjing Huang. 2017. Adversarial multi-criteria learning for chinese word segmentation. arXiv preprint arXiv:1704.07556 (2017).

[6]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).

[7]

Jingtao Ding, Guanghui Yu, Xiangnan He, Yuhan Quan, Yong Li, Tat-Seng Chua, Depeng Jin, and Jiajie Yu. 2018. Improving implicit recommender systems with view data. In IJCAI. 3343–3349.

[8]

Ali Mamdouh Elkahky, Yang Song, and Xiaodong He. 2015. A multi-view deep learning approach for cross domain user modeling in recommendation systems. In WWW. 278–288.

[9]

Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, and Depeng Jin. 2019. Neural multi-task recommendation from multi-behavior data. In ICDE. 1554–1557.

[10]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW. 173–182.

[11]

Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, and Yann LeCun. 2017. Tracking the world state with recurrent entity networks. In ICLR.

[12]

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).

[13]

Jizhou Huang, Wei Zhang, Yaming Sun, Haifeng Wang, and Ting Liu. 2018. Improving entity recommendation with search log and multi-task learning. In IJCAI. 4107–4114.

[14]

Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR.

[15]

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In NIPS. 1097–1105.

Digital Library

[16]

Artus Krohn-Grimberghe, Lucas Drumond, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2012. Multi-relational matrix factorization using bayesian personalized ranking for social network data. In WSDM. 173–182.

[17]

Zhi Li, Hongke Zhao, Qi Liu, Zhenya Huang, Tao Mei, and Enhong Chen. 2018. Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors. In SIGKDD. 1734–1743.

[18]

Babak Loni, Roberto Pagano, Martha Larson, and Alan Hanjalic. 2016. Bayesian personalized ranking with multi-channel user feedback. In Proceedings of the 10th ACM Conference on Recommender Systems. 361–364.

Digital Library

[19]

Babak Loni, Roberto Pagano, Martha Larson, and Alan Hanjalic. 2019. Top-N recommendation with multi-channel positive feedback using factorization machines. ACM Trans. Inf. Syst. 37, 2 (2019), 15:1–15:23.

Digital Library

[20]

Chen Ma, Peng Kang, and Xue Liu. 2019. Hierarchical gating networks for sequential recommendation. In SIGKDD. 825–833.

[21]

Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, Nov (2008), 2579–2605.

[22]

Yabo Ni, Dan Ou, Shichen Liu, Xiang Li, Wenwu Ou, Anxiang Zeng, and Luo Si. 2018. Perceive your users in depth: Learning universal user representations from multiple e-commerce tasks. In SIGKDD. 596–605.

[23]

Huihuai Qiu, Yun Liu, Guibing Guo, Zhu Sun, Jie Zhang, and Hai Thanh Nguyen. 2018. BPRH: Bayesian personalized ranking for heterogeneous implicit feedback. Information Sciences 453 (2018), 80–98.

[24]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In 25th Conference on Uncertainty in Artificial Intelligence. 452–461.

[25]

Michael L. Seltzer and Jasha Droppo. 2013. Multi-task learning in deep neural networks for improved phoneme recognition. In IEEE International Conference on Acoustics, Speech and Signal Processing. 6965–6969.

[26]

Ajit P. Singh and Geoffrey J. Gordon. 2008. Relational learning via collective matrix factorization. In SIGKDD. 650–658.

[27]

Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In CIKM. 1441–1450.

[28]

Md Mehrab Tanjim, Congzhe Su, Ethan Benjamin, Diane Hu, Liangjie Hong, and Julian McAuley. 2020. Attentive sequential models of latent intent for next item recommendation. In WWW. 2528–2534.

[29]

Mengting Wan and Julian J. McAuley. 2018. Item recommendation on monotonic behavior chains. In RecSys. 86–94.

[30]

Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2019. Multi-task feature learning for knowledge graph enhanced recommendation. In WWW. 2000–2010.

[31]

Shoujin Wang, Liang Hu, Yan Wang, Quan Z. Sheng, Mehmet A. Orgun, and Longbing Cao. 2019. Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks. In IJCAI. 3771–3777.

[32]

Shuai Zhang, Yi Tay, Lina Yao, Aixin Sun, and Jake An. 2019. Next item recommendation with self-attentive metric learning. In AAAI Workshop on Recommender Systems and Natural Language Processing, Vol. 9.

[33]

Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaoou Tang. 2014. Facial landmark detection by deep multi-task learning. In European Conference on Computer Vision. 94–108.

[34]

Zhe Zhao, Zhiyuan Cheng, Lichan Hong, and Ed H. Chi. 2015. Improving user topic interest profiles by behavior factorization. In WWW. 1406–1416.

[35]

Chang Zhou, Jinze Bai, Junshuai Song, Xiaofei Liu, Zhengchao Zhao, Xiusi Chen, and Jun Gao. 2018. ATRank: An attention-based user behavior modeling framework for recommendation. In AAAI. 4564–4571.

[36]

Yu Zhu, Yu Gong, Qingwen Liu, Yingcai Ma, Wenwu Ou, Junxiong Zhu, Beidou Wang, Ziyu Guan, and Deng Cai. 2019. Query-based interactive recommendation by meta-path and adapted attention-GRU. In CIKM. 2585–2593.

[37]

Yu Zhu, Junxiong Zhu, Jie Hou, Yongliang Li, Beidou Wang, Ziyu Guan, and Deng Cai. 2018. A brand-level ranking system with the customized attention-GRU model. In IJCAI. 3947–3953.

Cited By

Ma YGan M(2024)Sequential-hierarchical attention network: Exploring the hierarchical intention feature in POI recommendationWorld Wide Web10.1007/s11280-024-01295-y27:6Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.1007/s11280-024-01295-y
Yan MCheng ZGao CSun JLiu FSun FLi H(2023)Cascading Residual Graph Convolutional Network for Multi-Behavior RecommendationACM Transactions on Information Systems10.1145/358769342:1(1-26)Online publication date: 15-Mar-2023
https://dl.acm.org/doi/10.1145/3587693
Xu HPeng QLiu HSun YWang W(2023)Group-Based Personalized News Recommendation with Long- and Short-Term Fine-Grained MatchingACM Transactions on Information Systems10.1145/358494642:1(1-27)Online publication date: 21-Feb-2023
https://dl.acm.org/doi/10.1145/3584946
Show More Cited By

Index Terms

Modeling Multiple Coexisting Category-Level Intentions for Next Item Recommendation
1. Applied computing
  1. Electronic commerce
    1. Online shopping
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank
    2. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 39, Issue 3

July 2021

432 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3450607

Editor:
Min Zhang
Tsinghua University, China

Issue’s Table of Contents

Copyright © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 May 2021

Accepted: 01 December 2020

Revised: 01 October 2020

Received: 01 May 2020

Published in TOIS Volume 39, Issue 3

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Funding Sources

2030 National Key AI Program of China
National Science Foundation of China
Shanghai Municipal Science and Technology Commission
Program for Changjiang Young Scholars in University of China
Program for China Top Young Talents
Program for Shanghai Top Young Talents
SJTU Global Strategic Partnership Fund (2019 SJTU-HKUST)
Oceanic Interdisciplinary Program of Shanghai Jiao Tong University
Scientific Research Fund of Second Institute of Oceanography

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Cited By

Ma YGan M(2024)Sequential-hierarchical attention network: Exploring the hierarchical intention feature in POI recommendationWorld Wide Web10.1007/s11280-024-01295-y27:6Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.1007/s11280-024-01295-y
Yan MCheng ZGao CSun JLiu FSun FLi H(2023)Cascading Residual Graph Convolutional Network for Multi-Behavior RecommendationACM Transactions on Information Systems10.1145/358769342:1(1-26)Online publication date: 15-Mar-2023
https://dl.acm.org/doi/10.1145/3587693
Xu HPeng QLiu HSun YWang W(2023)Group-Based Personalized News Recommendation with Long- and Short-Term Fine-Grained MatchingACM Transactions on Information Systems10.1145/358494642:1(1-27)Online publication date: 21-Feb-2023
https://dl.acm.org/doi/10.1145/3584946
He MLin JLuo JPan WMing Z(2022)FLAG: A Feedback-aware Local and Global Model for Heterogeneous Sequential RecommendationACM Transactions on Intelligent Systems and Technology10.1145/355704614:1(1-22)Online publication date: 9-Nov-2022
https://dl.acm.org/doi/10.1145/3557046
Zhao SWu RTao JQu MZhao MFan CZhao H(2022)perCLTV: A General System for Personalized Customer Lifetime Value Prediction in Online GamesACM Transactions on Information Systems10.1145/353001241:1(1-29)Online publication date: 23-Apr-2022
https://dl.acm.org/doi/10.1145/3530012

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