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山东大学学报 (工学版) ›› 2020, Vol. 50 ›› Issue (1): 21-27.doi: 10.6040/j.issn.1672-3961.0.2019.411

• 机器学习与数据挖掘 • 上一篇    下一篇

基于门控循环单元与主动学习的协同过滤推荐算法

陈德蕾1(),王成1,*(),陈建伟2,吴以茵1   

  1. 1. 华侨大学计算机科学与技术学院, 福建 厦门 361021
    2. 圣地亚哥州立大学数学与统计学院, 加利福尼亚州 圣地亚哥 92182
  • 收稿日期:2019-01-03 出版日期:2020-02-20 发布日期:2020-02-14
  • 通讯作者: 王成 E-mail:18013083003@hqu.edu.cn;wangcheng@hqu.edu.cn
  • 作者简介:陈德蕾(1993-),男,浙江温州人,硕士研究生,主要研究方向为机器学习和数据挖掘. E-mail:18013083003@hqu.edu.cn
  • 基金资助:
    福建省引导性科技计划资助项目(2017H01010065)

GRU-based collaborative filtering recommendation algorithm with active learning

Delei CHEN1(),Cheng WANG1,*(),Jianwei CHEN2,Yiyin WU1   

  1. 1. College of Computer Science and Technology, Huaqiao University, Xiamen 361021, Fujian, China
    2. Department of Mathematics and Statistics, San Diego State University, San Diego 92182, CA, USA
  • Received:2019-01-03 Online:2020-02-20 Published:2020-02-14
  • Contact: Cheng WANG E-mail:18013083003@hqu.edu.cn;wangcheng@hqu.edu.cn
  • Supported by:
    福建省引导性科技计划资助项目(2017H01010065)

摘要:

针对传统协同过滤推荐算法存在无法反映用户短时兴趣的问题,提出一种基于门控循环单元(gated recurrent unit, GRU)神经网络与主动学习的协同过滤推荐算法。在采用GRU神经网络的基础上,将数据进行时序化处理,反映用户兴趣变化,并利用主动学习动态采样数据中的高质量的数据进行GRU神经网络的训练,使模型快速建立。在MovieLens1M数据集上的试验结果表明:加入主动学习的GRU模型的推荐算法比基于用户的协同过滤推荐算法(user-based collaborative filtering, UCF)、基于马尔科夫模型的协同过滤推荐算法(markov chain, MC)、基于隐语义模型的协同过滤推荐算法(latent factor model, LFM)算法有更高的短时预测率、召回率、项目覆盖率以及用户覆盖数,能够有效预测用户短时兴趣,提升精度,发掘长尾物品,且与原始GRU模型相比能够以更少的迭代次数达到相同效果。

关键词: 协同过滤, 门控循环单元, 主动学习, 深度学习, 时序化数据

Abstract:

The traditional collaborative filtering recommendation algorithm failed to reflect short-term user interest. In order to reflect the short-term interests of users better, a collaborative filtering recommendation algorithm based on Gated Recurrent Unit (GRU) neural network with active learning was proposed. Based on the GRU neural network, the algorithm processed the data into time-series data to reflect the change of the user's interest and used active learning to sample high-quality data dynamically for accelerating the training of GRU neural network. The result on MovieLens1M dataset showed that the GRU model with active learning could obtain higher short-term prediction success rate, recall rate, item coverage, and user coverage compared with the user-based collaborative filtering method (UCF), the markovian chain model-based collaborative filtering method (MC) and the matrix factory-based collaborative filtering method (LFM), so it could effectively predict the short-term interest of users, improve the accuracy, discover the long-tail items. Meanwhile, it could achieve the same effect with fewer iterations compared with the original GRU model.

Key words: collaborative filtering, gated recurrent unit, active learning, deep learning, time-series data

中图分类号: 

  • TP311

图1

算法步骤流程图"

表1

基于门控循环单元的主动学习推荐方法与其它推荐算法的比较"

指标 UCF MC LFM GRU+Active
使用数据 原始评分矩阵 时序化评分数据 原始评分矩阵 时序化评分数据
反映短时用户兴趣 一般
短时预测精度 一般
发掘长尾物品能力 一般

表2

GRU模型与GRU+Active模型参数表"

方法 参数
GRU+随机采样 l=0.05, k=50, adam
GRU+Active a=100, l=0.05, k=50, adam

表3

各算法在用户电影评分数据集上预测性能"

方法 Sps/% Recall/% Item-coverage/个 User-coverage/%
UCF 11.62 5.77 301 77.99
LFM 11.28 5.65 399 80.80
MC 19.54 5.27 558 78.70
GRU 32.16 8.85 659 87.98
GRU+Active 32.51 8.86 662 88.81

图2

GRU和GRU+Active的Sps效果对比图"

图3

GRU和GRU+Active的召回率效果对比图"

图4

GRU和GRU+Active的用户覆盖率效果对比图"

图5

GRU和GRU+Active的项目覆盖数效果对比图"

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