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Towards more effective encoders in pre-training for sequential recommendation

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Abstract

Pre-training emerges as a new learning paradigm in natural language processing and computer vision. It has also been introduced into sequential recommendation in several seminal studies for alleviating data sparsity issue. However, existing methods adopt the bidirectional transformer as the encoder which suffers from two drawbacks. One is insufficient intention modeling since the transformer architecture is suitable for extracting distributed consumption intention but cannot well catch users’ concentrated and occasion consumption intentions. The other is information leakage caused by foreseeing the future item in advance during the bidirectional encoding process. To address these problems, we propose to construct more effective encoders in pre-training for sequential recommendation. Specifically, we first decouple the original bidirectional process in transformer structure into two unidirectional processes which can avoid the information leakage problem and capture the distributed consumption intention. We then employ the locality-aware convolutional neural networks (CNNs) with narrow receptive field for concentrated consumption modeling. We also introduce a random shuffle strategy to empower CNN with the ability of modeling the occasion consumption. Experiments on five datasets demonstrate that our method improves the performance of various types of downstream sequential recommendation models to a large extent, and it also generates the overall better performance than the state-of-the-art self-supervised pre-training methods.

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Notes

  1. On the Amazon and LastFM datasets used in our experiments, we do not observe sufficient periodical repeat item records in Amazon, and most repeat music records of LastFM are in a loop style. Though the periodical repeat category records can be observed in both datasets, modeling them requires extra information and is unfair to other baseline methods along this line.

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Acknowledgements

This work was supported by a grant from the National Natural Science Foundation of China (NSFC) project (No. 62276193) and a grant from the Key Laboratory of Satellite Information Intelligent Processing and Application Technology (No. 2022-ZZKY-JJ-16-01). It was also supported by the Joint Lab. on Credit Sci. and Tech. of CSCI-Wuhan University.

Funding

This work was supported by a grant from the National Natural Science Foundation of China (NSFC) project (No. 62276193). It was also supported by a grant from the Key Laboratory of Satellite Information Intelligent Processing and Application Technology (No. 2022-ZZKY-JJ-16-01).

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, China

    Ke Sun, Tieyun Qian & Ming Zhong

  2. School of Information Management, Wuhan University, Wuhan, China

    Xuhui Li

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  1. Ke Sun
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  2. Tieyun Qian
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  3. Ming Zhong
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ke Sun and Tieyun Qian. The first draft of the manuscript was written by Ke Sun and revised by Tieyun Qian. Ming Zhong and Xuhui Li helped perform the analysis with constructive discussions. All authors read and approved the final manuscript.

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Correspondence to Tieyun Qian.

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Sun, K., Qian, T., Zhong, M. et al. Towards more effective encoders in pre-training for sequential recommendation. World Wide Web 26, 2801–2832 (2023). https://doi.org/10.1007/s11280-023-01163-1

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