Framework of feature fusion and distribution with mixture of experts for parallel recommendation algorithm

被引：0
作者：
Yang Z. [1 ,2 ]
Ge H.-W. [1 ,2 ]
Li T. [1 ,2 ]
机构：
[1] School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi
[2] Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Wuxi
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2023年 / 57卷 / 07期
关键词：
click-through rate prediction; deep learning; mixture of experts; recommender system;
D O I：
10.3785/j.issn.1008-973X.2023.07.006
中图分类号：
学科分类号：
摘要：
A mixture of experts parallel recommendation algorithm framework which combined feature fusion and distribution was proposed in order to address the issues of parameter sharing and high computational costs in click-through rate prediction. The ability of parallel architecture can be improved to distinguish different types of features and learn more expressive feature inputs, and parameters between explicit and implicit features can be shared. The gradients during backpropagation were mitigated and the performance of the model was improved. The framework is lightweight and model-agnostic, and can be generalized to a variety of mainstream parallel recommendation algorithms. Extensive experimental results on three public datasets demonstrate that the algorithm framework can be used to effectively improve the performance of SOTA models. © 2023 Zhejiang University. All rights reserved.
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收藏
页码：1317 / 1325
页数：8
相关论文
共 21 条
  • [1] KHAWAR F, HANG X, TANG R, Et al., Autofeature: searching for feature interactions and their architectures for click-through rate prediction [C], Proceedings of the 29th ACM International Conference on Information and Knowledge Management, pp. 625-634, (2020)
  • [2] HU D, WANG C, NIE F, Et al., Dense multimodal fusion for hierarchically joint representation [C], 2019 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3941-3945, (2019)
  • [3] CHENG H T, KOC L, HARMSEN J, Et al., Wide and deep learning for recommender systems [C], Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7-10, (2016)
  • [4] RENDLE S., Factorization machines [C], IEEE International Conference on Data Mining, pp. 995-1000, (2010)
  • [5] GUO H, TANG R, YE Y, Et al., DeepFM: a factorization-machine based neural network for CTR prediction [C], Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 1725-1731, (2017)
  • [6] WANG R, SHIVANNA R, CHENG D, Et al., DCN v2: improved deep & cross network and practical lessons for web-scale learning to rank systems [C], Proceedings of the Web Conference, pp. 1785-1797, (2021)
  • [7] BEUTEL A, COVINGTON P, JAIN S, Et al., Latent cross: making use of context in recurrent recommender systems [C], Proceedings of the 11th ACM International Conference on Web Search and Data Mining, pp. 46-54, (2018)
  • [8] QU Y, FANG B, ZHANG W, Et al., Product-based neural networks for user response prediction over multi-field categorical data [J], ACM Transactions on Information Systems, 37, 1, pp. 1-35, (2018)
  • [9] ZHOU G, ZHU X, SONG C, Et al., Deep interest network for click-through rate prediction [C], Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1059-1068, (2018)
  • [10] ZHOU G, MOU N, FAN Y, Et al., Deep interest evolution network for click-through rate prediction [C], Proceedings of the AAAI Conference on Artificial Intelligence, pp. 5941-5948, (2019)
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