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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (3): 78-87    DOI: 10.11925/infotech.2096-3467.2020.0328
Current Issue | Archive | Adv Search |
Recommendation Model Incorporating Neighbor Reviews for GRU Products
Feng Yong1,Liu Yang1,Xu Hongyan1,Wang Rongbing1(),Zhang Yonggang2
1College of Information, Liaoning University, Shenyang 110036, China
2Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University, Changchun 130012, China
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[Objective] This paper improves the matrix factorization algorithm with neighboring user’s comments, aiming to address the sparse comments issue and improve recommendation accuracy. [Methods] First, we used the Multi-layer Perceptron to improve the matrix decomposition algorithm and obtain the deep nonlinear features of users and commodities. Then, we processed the reviews and integrated the characteristics of users and their neighbors. Third, we identified users’ features in line with their preferences. Finally, we made recommendations based on the obtained prediction scores of the features. [Results] We compared the performance of our new algorithm with other models on the Amazon dataset. The accuracy, recall, and normalized cumulative loss gain of the proposed model increased by up to 8.3%, 22.8%, and 14.9%, respectively. [Limitations] We neither included the time factor of the user’s comments, nor excluded the fake comments. [Conclusions] Our new algorithm could effectively improve the recommendation results.

Key wordsRecommendation Model      Rating      Reviews      Sparsity      Deep Learning     
Received: 22 October 2020      Published: 24 November 2020
ZTFLH:  TP302  
Fund:Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education(93K172018K01);Social Science Planning Foundation of Liaoning Province of China(L18AGL007);CERNET Innovation Project(NGII20190301)
Corresponding Authors: Wang Rongbing     E-mail:

Cite this article:

Feng Yong,Liu Yang,Xu Hongyan,Wang Rongbing,Zhang Yonggang. Recommendation Model Incorporating Neighbor Reviews for GRU Products. Data Analysis and Knowledge Discovery, 2021, 5(3): 78-87.

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GRU-NR Model
Deep Fusion of User Features and Neighbor Features
数据集 用户数 商品数 样本数 稀疏度 用户发表平均评论数 商品收到平均评论数
Toys_and_Games 19 412 11 924 167 597 99.92% 8.63 14.05
Digital_Music 5 541 3 568 64 706 99.67% 11.67 18.14
Automotive 2 928 1 835 20 473 99.62% 7.23 20.35
Basic Information About Data Sets
Toys_and_Games 1.246 2 1.041 6 0.982 6 0.934 0 0.926 1 0.901 0
Digital_Music 1.095 9 0.963 8 0.912 5 0.897 3 0.871 3 0.865 3
Automotive 1.168 8 1.008 0 0.943 4 0.911 6 0.912 4 0.894 2
Comparison of Score Prediction Accuracy
Recall Values of Recommended Lists for Each Model
NDCG Values of Recommended Lists for Each Model
Applicability Analysis of Neighbor Reviews
方法 方法描述 相似度(偏好) 运算速度排序 是否选择其他商品的评论 差异性 推荐准确性(RMSE)
方法1 选出与当前商品评分一致的用户,直接将其评论作为近邻评论 0.372 1 0.951 9
方法2 选出与当前商品评论语义相似度最高的评论作为近邻评论 0.796 3 0.933 7
方法3 本文方法 0.715 2 0.894 2
Comparison of Neighbor Reviews Selection Methods
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