Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (1): 21-27.doi: 10.6040/j.issn.1672-3961.0.2019.411

• Machine Learning & Data Mining • Previous Articles     Next Articles

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)

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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

CLC Number: 

  • TP311

Fig.1

Algorithm flow chart"

Table 1

Comparison of GRU+Active with other recommendation algorithms"

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 差 一般 差 好
短时预测精度 低 一般 低 高
发掘长尾物品能力 差 一般 差 好

Table 2

Parameters values used in the GRU Modeland the GRU+Active Model"

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

Table 3

The predictive performance of each algorithm on movielens dataset"

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

Fig.2

Comparison of GRU and GRU+Active in Sps"

Fig.3

Comparison of GRU and GRU+Active in recall"

Fig.4

Comparison of GRU and GRU+Active in user coverage"

Fig.5

Comparison of GRU and GRU+Active in item coverage"

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