research-article

CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment

Authors:

Jiandong ZhangAuthors Info & Claims

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

Pages 278 - 287

https://doi.org/10.1145/3523227.3546773

Published: 13 September 2022 Publication History

Abstract

Traditional recommendation systems mainly focus on modeling user interests. However, the dynamics of recommended items caused by attribute modifications (e.g. changes in prices) are also of great importance in real systems, especially in the fast-growing e-commerce environment, which may cause the users’ demands to emerge, shift and disappear. Recent studies that make efforts on dynamic item representations treat the item attributes as side information but ignore its temporal dependency, or model the item evolution with a sequence of related users but do not consider item attributes. In this paper, we propose Core Attribute Evolution Network (CAEN), which partitions the user sequence according to the attribute value and thus models the item evolution over attribute dynamics with these users. Under this framework, we further devise a hierarchical attention mechanism that applies attribute-aware attention for user aggregation under each attribute, as well as personalized attention for activating similar users in assessing the matching degree between target user and item. Results from the extensive experiments over actual e-commerce datasets show that our approach outperforms the state-of-art methods and achieves significant improvements on the items with rapid changes over attributes, therefore helping the item recommendation to adapt to the growth of the e-commerce platform.

Supplementary Material

MOV File (CAEN-Recsys22_final.mov)

Presentation video

Download
263.94 MB

References

[1]

Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018. Sequential recommendation with user memory networks. In Proceedings of the eleventh ACM international conference on web search and data mining. 108–116.

Digital Library

[2]

Chen Cheng, Haiqin Yang, Michael R Lyu, and Irwin King. 2013. Where you like to go next: Successive point-of-interest recommendation. In Twenty-Third international joint conference on Artificial Intelligence.

[3]

Sung Min Cho, Eunhyeok Park, and Sungjoo Yoo. 2020. MEANTIME: Mixture of Attention Mechanisms with Multi-temporal Embeddings for Sequential Recommendation. In Fourteenth ACM Conference on Recommender Systems. 515–520.

[4]

Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems. 191–198.

Digital Library

[5]

Guibing Guo, Shichang Ouyang, Xiaodong He, Fajie Yuan, and Xiaohua Liu. 2019. Dynamic Item Block and Prediction Enhancing Block for Sequential Recommendation. In IJCAI. 1373–1379.

[6]

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based Recommendations with Recurrent Neural Networks. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1511.06939

[7]

Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 197–206.

[8]

Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 426–434.

Digital Library

[9]

Xiang Li, Chao Wang, Bin Tong, Jiwei Tan, Xiaoyi Zeng, and Tao Zhuang. 2020. Deep Time-Aware Item Evolution Network for Click-Through Rate Prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 785–794.

Digital Library

[10]

Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang. 2016. Product-based neural networks for user response prediction. In 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, 1149–1154.

[11]

Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web. 811–820.

Digital Library

[12]

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 Proceedings of the 28th ACM international conference on information and knowledge management. 1441–1450.

Digital Library

[13]

Xiaoxin Sun, Lisa Zhang, Yuling Wang, Mengying Yu, Minghao Yin, and Bangzuo Zhang. 2021. Attribute-aware deep attentive recommendation. The Journal of Supercomputing 77, 6 (2021), 5510–5527.

Digital Library

[14]

Qiaoyu Tan, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren Zhou, and Xia Hu. 2021. Dynamic Memory based Attention Network for Sequential Recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 4384–4392.

[15]

Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. 565–573.

Digital Library

[16]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998–6008.

[17]

Chenyang Wang, Min Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma. 2020. Make it a chorus: knowledge-and time-aware item modeling for sequential recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 109–118.

Digital Library

[18]

Jia Wang, Vincent W Zheng, Zemin Liu, and Kevin Chen-Chuan Chang. 2017. Topological recurrent neural network for diffusion prediction. In 2017 IEEE International Conference on Data Mining (ICDM). IEEE, 475–484.

[19]

Le Wu, Yonghui Yang, Kun Zhang, Richang Hong, Yanjie Fu, and Meng Wang. 2020. Joint item recommendation and attribute inference: An adaptive graph convolutional network approach. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 679–688.

Digital Library

[20]

Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical attention networks for document classification. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies. 1480–1489.

[21]

Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, Yanchi Liu, Guandong Xu, Xing Xie, Hui Xiong, and Jian Wu. 2018. Sequential recommender system based on hierarchical attention network. In IJCAI International Joint Conference on Artificial Intelligence.

[22]

Feng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2020. TAGNN: Target attentive graph neural networks for session-based recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1921–1924.

Digital Library

[23]

Yonghong Yu, Can Wang, Hao Wang, and Yang Gao. 2017. Attributes coupling based matrix factorization for item recommendation. Applied Intelligence 46, 3 (2017), 521–533.

Digital Library

[24]

Xu Yuan, Dongsheng Duan, Lingling Tong, Lei Shi, and Cheng Zhang. 2021. ICAI-SR: Item Categorical Attribute Integrated Sequential Recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1687–1691.

Digital Library

[25]

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 Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.

[26]

Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 5941–5948.

Digital Library

[27]

Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1059–1068.

Digital Library

Cited By

Tang JFan YDu YLi XChen X(2024)A feature-aware long-short interest evolution network for sequential recommendationIntelligent Data Analysis10.3233/IDA-23028828:3(733-750)Online publication date: 28-May-2024
https://dl.acm.org/doi/10.3233/IDA-230288
Fetahu BCohen NHaramaty ELewin-Eytan LRokhlenko OMalmasi S(2024)Identifying Shopping Intent in Product QA for Proactive RecommendationsAdvances on Graph-Based Approaches in Information Retrieval10.1007/978-3-031-71382-8_3(25-40)Online publication date: 10-Oct-2024
https://doi.org/10.1007/978-3-031-71382-8_3
Ji XYang PHao QZheng WXiao Y(2023)Item Multi-Information Evolution Network for Click-Through Rate Prediction2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD57460.2023.10152622(285-290)Online publication date: 24-May-2023
https://doi.org/10.1109/CSCWD57460.2023.10152622

Index Terms

CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. World Wide Web
    1. Web searching and information discovery
      1. Content ranking
      2. Personalization

Recommendations

Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts

Many e-commerce sites present additional item recommendations to their visitors while they navigate the site, and ample evidence exists that such recommendations are valuable for both customers and providers. Academic research often focuses on the ...
Leveraging attribute latent features for addressing new item cold-start issue
Highlights
- Integrates CF, CB, and neural network technologies.
- Shows the advantage of ...
Abstract
A recommender system employs an information filtering technology aiming to recommend items that are likely to be of interest to users, based on user behavior, rating feedback of items, or item characteristics. The cold-start problem ...
Item-based top-N recommendation algorithms

The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of items that will be of interest to a certain user. ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

September 2022

743 pages

ISBN:9781450392785

DOI:10.1145/3523227

Copyright © 2022 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 ACM 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 September 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

RecSys '22

Sponsor:

RecSys '22: Sixteenth ACM Conference on Recommender Systems

September 18 - 23, 2022

WA, Seattle, USA

Acceptance Rates

Overall Acceptance Rate 254 of 1,295 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
589
Total Downloads

Downloads (Last 12 months)49
Downloads (Last 6 weeks)2

Reflects downloads up to 22 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Tang JFan YDu YLi XChen X(2024)A feature-aware long-short interest evolution network for sequential recommendationIntelligent Data Analysis10.3233/IDA-23028828:3(733-750)Online publication date: 28-May-2024
https://dl.acm.org/doi/10.3233/IDA-230288
Fetahu BCohen NHaramaty ELewin-Eytan LRokhlenko OMalmasi S(2024)Identifying Shopping Intent in Product QA for Proactive RecommendationsAdvances on Graph-Based Approaches in Information Retrieval10.1007/978-3-031-71382-8_3(25-40)Online publication date: 10-Oct-2024
https://doi.org/10.1007/978-3-031-71382-8_3
Ji XYang PHao QZheng WXiao Y(2023)Item Multi-Information Evolution Network for Click-Through Rate Prediction2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD57460.2023.10152622(285-290)Online publication date: 24-May-2023
https://doi.org/10.1109/CSCWD57460.2023.10152622

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents