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Frontiers of Computer Science

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ISSN 2095-2236(Online)

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Front. Comput. Sci.    2024, Vol. 18 Issue (3) : 183341    https://doi.org/10.1007/s11704-023-2703-y
Artificial Intelligence
BMLP: behavior-aware MLP for heterogeneous sequential recommendation
Weixin LI1, Yuhao WU1, Yang LIU1, Weike PAN1(), Zhong MING1,2
1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
2. Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518123, China
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Corresponding Author(s): Weike PAN   
Just Accepted Date: 14 November 2023   Issue Date: 08 January 2024
 Cite this article:   
Weixin LI,Yuhao WU,Yang LIU, et al. BMLP: behavior-aware MLP for heterogeneous sequential recommendation[J]. Front. Comput. Sci., 2024, 18(3): 183341.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-023-2703-y
https://academic.hep.com.cn/fcs/EN/Y2024/V18/I3/183341
Fig.1  The overview of our proposed BMLP
Dataset Metric GRU4Rec[1] SRGNN[4] BERT4Rec[8] SASRec[3] RIB[9] BINN[2] MSR[5] MLP-Mixer[10] FMLP[11] BMLP
Rec15 HR@10 0.437 0.483 0.389 0.437 0.387 0.468 0.605 0.478 0.541 0.618
NDCG@10 0.249 0.285 0.196 0.248 0.213 0.257 0.378 0.259 0.255 0.407
Tmall HR@10 0.352 0.274 0.168 0.369 0.269 0.298 0.298 0.292 0.381 0.426
NDCG@10 0.239 0.187 0.096 0.281 0.188 0.199 0.195 0.203 0.309 0.315
ML1M HR@10 0.268 0.249 0.145 0.243 0.229 0.273 0.265 0.264 0.116 0.304
NDCG@10 0.152 0.141 0.072 0.132 0.122 0.155 0.150 0.145 0.058 0.179
UB HR@10 0.171 0.142 0.098 0.181 0.132 0.141 0.172 0.145 0.158 0.200
NDCG@10 0.104 0.083 0.055 0.106 0.080 0.083 0.099 0.085 0.103 0.114
Tab.1  The overall performance of our BMLP and nine baselines. Notice that the best results are marked in bold and the second best are underlined
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6 Jinwei LUO, Mingkai HE, Weike PAN, Zhong MING. BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation. Frontiers of Computer Science, 2023, 17(5): 175336
7 Zhuo-Xin ZHAN, Ming-Kai HE, Wei-Ke PAN, Zhong MING. TransRec++: Translation-based sequential recommendation with heterogeneous feedback. Frontiers of Computer Science, 2022, 16(2): 162615
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10 I, Tolstikhin N, Houlsby A, Kolesnikov L, Beyer X, Zhai T, Unterthiner J, Yung A, Steiner D, Keysers J, Uszkoreit M, Lucic A Dosovitskiy . MLP-Mixer: an all-MLP architecture for vision. In: Proceedings of the 35th Conference on Neural Information Processing Systems. 2021
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[1] FCS-22703-OF-WL_suppl_1 Download
[2] FCS-22703-OF-WL_suppl_2 Download
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