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FwSeqBlock: A Field-wise Approach for Modeling Behavior Representation in Sequential Recommendation

Published: 17 October 2022 Publication History

Abstract

Modeling users' historical behaviors is an essential task in many industrial recommender systems. The user interest representation, in previous works, is obtained through the following paradigm: concrete behaviors are firstly embedded as low-dimensional behavior representations, which are then aggregated conditioning on the target item for final user interest representation. Most existing researches focus on the aggregation process that explores the intrinsic structure of the behavior sequences. However, the quality of behavior representation is largely ignored. In this paper, we present a pluggable module, FwSeqBlock, to enhance the expressiveness of behavior representations. Specifically, FwSeqBlock introduces the multiplicative operation among users' historical behaviors and the target item, where a field memory unit is designed to dynamically identify the dominant features from the behavior sequence and filter out the noise. Extensive experiments validate that FwSeqBlock consistently generates higher-quality user representations compared with competitive methods. Besides, online A/B testing reports a 4.46% improvement in Click-Through Rate (CTR), confirming the effectiveness of the proposed method.

References

[1]
Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016. Layer Normalization. CoRR, Vol. abs/1607.06450 (2016). showeprint[arXiv]1607.06450 http://arxiv.org/abs/1607.06450
[2]
Alex Beutel, Paul Covington, Sagar Jain, Can Xu, Jia Li, Vince Gatto, and Ed H. Chi. 2018. Latent Cross: Making Use of Context in Recurrent Recommender Systems. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (Marina Del Rey, CA, USA) (WSDM '18). Association for Computing Machinery, New York, NY, USA, 46--54. https://doi.org/10.1145/3159652.3159727
[3]
Qiwei Chen, Huan Zhao, Wei Li, Pipei Huang, and Wenwu Ou. 2019. Behavior Sequence Transformer for E-Commerce Recommendation in Alibaba., Article 12 (2019), 4 pages. https://doi.org/10.1145/3326937.3341261
[4]
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv:1412.3555 (2014).
[5]
Tom Fawcett. 2006. An introduction to ROC analysis. Pattern Recognition Letters, Vol. 27, 8 (2006), 861--874.
[6]
Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, and Keping Yang. 2019. Deep Session Interest Network for Click-through Rate Prediction. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (Macao, China) (IJCAI'19). AAAI Press, 2301--2307.
[7]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770--778. https://doi.org/10.1109/CVPR.2016.90
[8]
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based Recommendations with Recurrent Neural Networks. arXiv:1511.06939 (2016).
[9]
Xiaowen Huang, Shengsheng Qian, Quan Fang, Jitao Sang, and Changsheng Xu. 2018. CSAN: Contextual Self-Attention Network for User Sequential Recommendation. In Proceedings of the 26th ACM International Conference on Multimedia (MM '18). 447--455. https://doi.org/10.1145/3240508.3240609
[10]
Qi Pi, Weijie Bian, Guorui Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4--8, 2019, Ankur Teredesai, Vipin Kumar, Ying Li, Ró mer Rosales, Evimaria Terzi, and George Karypis (Eds.). ACM, 2671--2679. https://doi.org/10.1145/3292500.3330666
[11]
Qi Pi, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, Xiaoqiang Zhu, and Kun Gai. 2020. Search-Based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (Virtual Event, Ireland) (CIKM '20). Association for Computing Machinery, New York, NY, USA, 2685--2692. https://doi.org/10.1145/3340531.3412744
[12]
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. J. Mach. Learn. Res., Vol. 15, 1, 1929--1958.
[13]
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 (CIKM '19). Association for Computing Machinery, New York, NY, USA, 1441--1450. https://doi.org/10.1145/3357384.3357895
[14]
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 (WSDM '18). 565--573. https://doi.org/10.1145/3159652.3159656
[15]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems, Vol. 30. Curran Associates, Inc.
[16]
Kai Zhang, Hao Qian, Qing Cui, Qi Liu, Longfei Li, Jun Zhou, Jianhui Ma, and Enhong Chen. 2021. Multi-Interactive Attention Network for Fine-Grained Feature Learning in CTR Prediction. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (Virtual Event, Israel) (WSDM '21). Association for Computing Machinery, New York, NY, USA, 984--992. https://doi.org/10.1145/3437963.3441761
[17]
Guorui Zhou, Weijie Bian, Kailun Wu, Lejian Ren, Qi Pi, Yujing Zhang, Can Xiao, Xiang-Rong Sheng, Na Mou, Xinchen Luo, Chi Zhang, Xianjie Qiao, Shiming Xiang, Kun Gai, Xiaoqiang Zhu, and Jian Xu. 2020. CAN: Revisiting Feature Co-Action for Click-Through Rate Prediction. arXiv: 2011.05625 (2020).
[18]
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. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 01, 5941--5948. https://doi.org/10.1609/aaai.v33i01.33015941
[19]
Guorui Zhou, Chengru Song, Xiaoqiang Zhu, Xiao Ma, Yanghui Yan, Xingya Dai, Han Zhu, Junqi Jin, Han Li, and Kun Gai. 2017. Deep Interest Network for Click-Through Rate Prediction. (2017).

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cover image ACM Conferences
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
October 2022
5274 pages
ISBN:9781450392365
DOI:10.1145/3511808
  • General Chairs:
  • Mohammad Al Hasan,
  • Li Xiong
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Published: 17 October 2022

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  1. recommender system
  2. sequential recommendation

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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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