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数据分析与知识发现  2020, Vol. 4 Issue (2/3): 68-77    DOI: 10.11925/infotech.2096-3467.2019.0728
  专辑 本期目录 | 过刊浏览 | 高级检索 |
基于多头自注意力神经网络的购物篮推荐方法*
倪维健,郭浩宇,刘彤(),曾庆田
山东科技大学计算机学院 青岛 266510
Online Product Recommendation Based on Multi-Head Self-Attention Neural Networks
Ni Weijian,Guo Haoyu,Liu Tong(),Zeng Qingtian
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266510, China
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摘要 

【目的】 针对用户一次购买多件物品的场景,为用户推荐下一次可能购买的多件物品。【方法】 基于多头自注意力神经网络设计一种新的购物篮推荐方法,该方法使用多头自注意力机制捕捉购物篮中不同物品的关系以及融合物品属性信息,并使用具有注意力的循环神经网络建模购物篮序列信息。【结果】 实验结果表明,本文方法优于传统推荐方法和现有基于深度学习的推荐方法,特别是在TaoBao数据集上F1值提升2%。【局限】 本文方法仅提升了推荐结果的准确性,是否能够提升多样性还需进一步验证。【结论】 多头自注意力能够更好地对购物篮进行建模,进而提升购物篮推荐效果。

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倪维健
郭浩宇
刘彤
曾庆田
关键词 购物篮推荐深度神经网络多头自注意力物品属性    
Abstract

[Objective] This paper aims to predict online customers’ future purchases based on their previous shopping behaviors.[Methods] We proposed a new product recommendation approach based on multi-head self-attention neural networks. Our method captured the relationship and attributes of items checked out by specific customers.Finally, we generated the recommended lists using recurrent neural networks with attentions.[Results] We examined the proposed approach on three real-world data sets and yielded better F1 values than existing methods (2% higher).[Limitations] The diversity of the recommended lists needs more analysis.[Conclusions] The multi-head self-attention mechanism is an effective way to model shopping behaviors and create better recommendations for the consumers.

Key wordsNext Basket Recommendation    Deep Neural Network    Multi-Head Self-Attention    Item Attributes
收稿日期: 2019-06-20     
中图分类号:  TP391  
基金资助:*本文系国家自然科学基金项目“面向用户群组的结构化推荐技术及其应用研究”(61602278);国家自然科学基金项目“应急预案流程图谱自动建模方法及其在场景式诊断中的应用”的研究成果之一(71704096)
通讯作者: 刘彤     E-mail: liu_tongtong@foxmail.com
引用本文:   
倪维健,郭浩宇,刘彤,曾庆田. 基于多头自注意力神经网络的购物篮推荐方法*[J]. 数据分析与知识发现, 2020, 4(2/3): 68-77.
Ni Weijian,Guo Haoyu,Liu Tong,Zeng Qingtian. Online Product Recommendation Based on Multi-Head Self-Attention Neural Networks. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2019.0728.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2019.0728
图1  多头自注意力模型结构
图2  基于多头自注意力的购物篮推荐模型结构
数据集 用户数量 物品数量 购物篮数量 物品属性数量
Ta-Feng 2 348 14 716 39 101 2 013
JingDong 16 891 17 718 250 996 10
TaoBao 1 235 11 623 16 937 1 619
表1  数据集基本信息(单位:个)
Model Precision Recall F1 Hit-Rate NDCG@5
TOP 0.0497 0.0698 0.0580 0.2172 0.0833
Item-CF 0.0272 0.0400 0.0324 0.1244 0.0431
NFM 0.0518 0.0728 0.0605 0.2432 0.0831
DREAM 0.0533 0.0781 0.0633 0.2322 0.0851
NAM 0.0599 0.0941 0.0732 0.2627 0.0925
ANAM 0.0600 0.0942 0.0733 0.2632 0.0925
本文方法 0.0624 0.0968 0.0759 0.2732 0.0940
表2  Ta-Feng数据集结果
Model Precision Recall F1 Hit-Rate NDCG@5
TOP 0.0140 0.0392 0.0206 0.0679 0.0270
Item-CF 0.0123 0.0341 0.0181 0.0584 0.0251
NFM 0.0337 0.0822 0.0478 0.1409 0.0633
DREAM 0.0213 0.0592 0.0314 0.0988 0.0413
NAM 0.0257 0.0678 0.0372 0.1150 0.0485
ANAM 0.0568 0.1530 0.0828 0.2294 0.1226
本文方法 0.0678 0.1859 0.0994 0.2620 0.1485
表3  JingDong数据集结果
Model Precision Recall F1 Hit-Rate NDCG@5
TOP 0.0011 0.0024 0.0015 0.0048 0.0025
Item-CF 0.0012 0.0034 0.0018 0.0056 0.0022
NFM 0.0074 0.0185 0.0106 0.0307 0.0136
DREAM 0.0020 0.0044 0.0028 0.0088 0.0038
NAM 0.0019 0.0042 0.0026 0.0080 0.0037
ANAM 0.0019 0.0046 0.0027 0.0088 0.0038
本文方法 0.0158 0.0477 0.0237 0.0704 0.0410
表4  TaoBao数据集结果
Model Precision Recall F1 Hit-Rate NDCG@5
-category-attention 0.0589 0.0935 0.0723 0.2629 0.0930
-category-transformer 0.0533 0.0746 0.0621 0.2317 0.0686
-multihead 0.0617 0.0961 0.0752 0.2687 0.0934
-attention 0.0601 0.0943 0.0734 0.2634 0.0787
-transformer 0.0556 0.0899 0.0687 0.2512 0.0836
完整网络 0.0624 0.0968 0.0759 0.2732 0.0940
表5  Ta-Feng数据集网络部分性能结果
Model Precision Recall F1 Hit-Rate NDCG@5
-category-attention 0.0428 0.1157 0.0625 0.1835 0.0919
-category-transformer 0.0559 0.1506 0.0816 0.2225 0.1232
-multihead 0.0665 0.1781 0.0969 0.2577 0.1480
-attention 0.0428 0.1223 0.0634 0.1820 0.0930
-transformer 0.0568 0.1539 0.0829 0.2304 0.1256
完整网络 0.0678 0.1859 0.0994 0.2620 0.1485
表6  JingDong数据集网络部分性能结果
Model Precision Recall F1 Hit-Rate NDCG@5
-category-attention 0.0080 0.0221 0.0118 0.0363 0.0178
-category-transformer 0.0046 0.0142 0.0070 0.0209 0.0101
-multihead 0.0171 0.0543 0.0260 0.0738 0.0450
-attention 0.0047 0.0143 0.0071 0.0272 0.0138
-transformer 0.0055 0.0145 0.0080 0.0252 0.0176
完整网络 0.0158 0.0477 0.0237 0.0704 0.0410
表7  TaoBao数据集网络部分性能结果
图3  基于物品的自注意力权重可视化
图4  基于属性的自注意力权重可视化
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