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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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Abstract [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.
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Received: 22 October 2020
Published: 24 November 2020
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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: wrb@lnu.edu.cn
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[1] |
侯银秀, 李伟卿, 王伟军, 等. 基于用户偏好与商品属性情感匹配的图书个性化推荐研究[J]. 数据分析与知识发现, 2017,1(8):9-17.
|
[1] |
( Hou Yinxiu, Li Weiqing, Wang Weijun, et al. Personalized Book Recommendation Based on User Preferences and Commodity Features[J]. Data Analysis and Knowledge Discovery, 2017,1(8):9-17.)
|
[2] |
Zheng L, Noroozi V, Yu P S. Joint Deep Modeling of Users and Items Using Reviews for Recommendation[C]// Proceedings of the 10th ACM International Conference on Web Search and Data Mining. ACM, 2017: 425-434.
|
[3] |
LeCun Y, Bottou L, Bengio Y, et al. Gradient-Based Learning Applied to Document Recognition[J]. Proceedings of the IEEE, 1998,86(11):2278-2324.
|
[4] |
Xing S N, Liu F A, Wang Q Q, et al. A Hierarchical Attention Model for Rating Prediction by Leveraging User and Product Reviews[J]. Neurocomputing, 2019,332(3):417-427.
|
[5] |
邢长征, 赵宏宝, 张全贵, 等. 融合评论文本层级注意力和外积的推荐方法[J]. 计算机科学与探索, 2020,14(6):947-957.
|
[5] |
( Xing Changzheng, Zhao Hongbao, Zhang Quangui, et al. Review Text Hierarchical Attention and Outer Product for Recommendation Method[J]. Journal of Frontiers of Computer Science and Technology, 2020,14(6):947-957.)
|
[6] |
Koren Y, Bell R, Volinsky C. Matrix Factorization Techniques for Recommender Systems[J]. Computer, 2009,42(8):30-37.
|
[7] |
Rosenblatt F. The Perceptron: A Perceiving and Recognizing Automaton[R]. Technical Report 85-460-1 of Project PARA. New York,USA: Cornell Aeronautical Laboratory, 1957.
|
[8] |
Chung J, Gulcehre C, Cho K H, et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling[OL]. arXiv Preprint, arXiv: 1412. 3555.
|
[9] |
Mnih A, Salakhutdinov R R. Probabilistic Matrix Factorization[C]// Proceedings of the Advances in Neural Information Processing Systems, Vancouver, Canada. Cambridge, MA: MIT Press, 2008: 1257-1264.
|
[10] |
宋瑞雪, 李国勇. 基于改进的矩阵分解模型在推荐系统中的应用[J]. 计算机应用, 2019,39(S1):93-95.
|
[10] |
( Song Ruixue, Li Guoyong. Application of Improved Matrix Factorization Model in Recommendation System[J]. Journal of Computer Applications, 2019,39(S1):93-95.)
|
[11] |
温彦, 马立健, 曾庆田, 等. 基于地理信息偏好修正和社交关系偏好隐式分析的POI推荐[J]. 数据分析与知识发现, 2019,3(8):30-40.
|
[11] |
( Wen Yan, Ma Lijian, Zeng Qingtian, et al. POI Recommendation Based on Geographic and Social Relationship Preferences[J]. Data Analysis and Knowledge Discovery, 2019,3(8):30-40.)
|
[12] |
李琳, 刘锦行, 孟祥福, 等. 融合评分矩阵与评论文本的商品推荐模型[J]. 计算机学报, 2018,41(7):1559-1573.
|
[12] |
( Li Lin, Liu Jinxing, Meng Xiangfu, et al. Recommendation Models by Exploiting Rating Matrix and Review Text[J]. Chinese Journal of Computers, 2018,41(7):1559-1573.)
|
[13] |
Chen C, Zhang M, Liu Y Q. Neural Attentional Rating Regression with Review-level Explanations[C]// Proceedings of the 2018 World Wide Web Conference. 2018: 1583-1592.
|
[14] |
Wu H, Zhang Z X, Yue K, et al. Dual-Regularized Matrix Factorization with Deep Neural Networks for Recommender Systems[J]. Knowledge-Based Systems, 2018,145:46-58.
|
[15] |
Pennington J, Socher R, Manning C. GloVe: Global Vectors Forword Representation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014: 1532-1543.
|
[16] |
Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[OL]. arXiv Preprint, arXiv: 1810. 04805.
|
[17] |
冯兴杰, 曾云泽. 基于评分矩阵与评论文本的深度推荐模型[J]. 计算机学报, 2020,43(5):884-900.
|
[17] |
( Feng Xingjie, Zeng Yunze. Joint Deep Modeling of Rating Matrix and Reviews for Recommendation[J]. Chinese Journal of Computers, 2020,43(5):884-900.)
|
[18] |
Feng X J, Zeng Y Z. Neural Collaborative Embedding from Reviews for Recommendation[J]. IEEE Access, 2019,7:103263-103274.
|
[19] |
Liu H T, Wang Y A, Peng Q Y, et al. Hybrid Neural Recommendation with Joint Deep Representation Learning of Ratings and Reviews[J]. Neurocomputing, 2020,374:77-85.
|
[20] |
冯兴杰, 曾云泽, 崔桂颖. 基于近邻用户评论的推荐辅助网络[J]. 计算机应用研究, 2020,37(10):2956-2960.
|
[20] |
( Feng Xingjie, Zeng Yunze, Cui Guiying. Recommendation Supplemental Network of Neighbor Reviews[J]. Application Research of Computers, 2020,37(10):2956-2960.)
|
[21] |
Gerard S, Christopher B. Term-Weighting Approaches in Automatic Text Retrieval[J]. Information Processing and Management, 1988,24(5):513-523.
|
[22] |
Rendle S. Factorization Machines[C]// Proceedings of 2010 IEEE International Conference on Data Mining. 2010: 995-1000.
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