Please wait a minute...
Data Analysis and Knowledge Discovery  2024, Vol. 8 Issue (1): 69-79    DOI: 10.11925/infotech.2096-3467.2022.1270
Current Issue | Archive | Adv Search |
Personalized Recommendation Algorithm with Review Sentiments and Importance
Li Hui,Hu Yaohua(),Xu Cunzhen
School of Economics and Management, Xidian University, Xi’an 710119, China
Download: PDF (946 KB)   HTML ( 18 ) 
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] To address the data sparsity issue and explore the impacts of emotional expression on user feature learning, this paper proposes a personalized recommendation algorithm based on sentiment and the importance of online reviews. [Methods] First, we used the BERT pre-trained language model to generate the vector representation of review texts. Then, we fed them into a Bi-GRU network to learn their semantic features. We also assigned weights to the review vector using sentiment weights and attention mechanisms. Finally, we utilized the DeepFM algorithm for deep interaction between user and product features to predict the user’s rating of the products. [Results] We examined the proposed model with the Amazon product data dataset. Our model reduced the RMSE and MAE metrics by up to 24.43% and 31.44% compared to the baseline models. Compared with the attention mechanism, our method reduced the RMSE and MAE metrics by up to 2.59% and 3.89%. [Limitations] The sentiment analysis method cannot represent the users’ emotional tendencies towards the different attributes of the product. [Conclusions] The proposed method considers the influence of user sentiment on user feature expression, improving the recommendation accuracy.
Key words： Recommendation Algorithm      Deep Learning      Sentiment Analysis      Attention Mechanism     
Received: 01 December 2022      Published: 12 September 2023
ZTFLH:  G203  
  TP391  
Fund:National Natural Science Foundation of China(71203173)
Corresponding Authors: Hu Yaohua，ORCID：0000-0001-9306-0974，E-mail：huyaohua@stu.xidian.edu.cn。   
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Hui Li
Yaohua Hu
Cunzhen Xu

Cite this article:

Li Hui, Hu Yaohua, Xu Cunzhen. Personalized Recommendation Algorithm with Review Sentiments and Importance. Data Analysis and Knowledge Discovery, 2024, 8(1): 69-79.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.1270     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2024/V8/I1/69

The Framework of PREEI
数据集 用户数 产品数 评论数 稀疏性
MI 1 429 900 10 261 99.20%
AIV 5 130 1 685 37 126 99.57%
OP 4 905 2 420 53 258 99.55%
THI 16 638 10 217 134 476 99.92%
VG 24 303 10 672 231 780 99.91%
Statistical Information of the Experimental Data Set
算法 MI AIV OP THI VG
RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE
PMF 0.995 0.846 1.150 0.935 0.991 0.805 1.108 0.920 1.204 0.962
ConvMF+ 0.942 0.713 1.033 0.771 0.877 0.655 1.020 0.748 1.083 0.818
DeepCoNN 0.897 0.797 1.004 0.792 0.870 0.738 0.961 0.760 1.129 0.910
NARRE 0.966 0.687 0.971 0.714 0.858 0.629 0.977 0.703 1.062 0.806
CARL 0.836 0.609 0.965 0.716 0.861 0.645 0.980 0.742 1.087 0.862
ATN 0.918 0.692 0.986 0.747 0.844 0.644 0.954 0.712 1.077 0.849
PREEI 0.822 0.587 0.869 0.641 0.836 0.644 0.890 0.642 1.010 0.768
Comparison of the Accuracy of Recommendation Algorithms
算法 MI AIV OP THI VG
RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE
PMF 17.39 30.61 24.43 31.44 15.64 20.00 19.68 30.22 16.11 20.17
ConvMF+ 12.74 17.67 15.88 16.86 4.68 1.68 12.75 14.17 6.74 6.11
DeepCoNN 8.36 26.35 13.45 19.07 3.91 12.74 7.39 15.53 10.54 15.60
NARRE 14.91 14.56 10.50 10.22 2.56 -2.38 8.90 8.68 4.90 4.71
CARL 1.79 3.45 9.85 10.47 2.90 0.16 9.18 13.48 7.17 10.70
ATN 10.46 15.17 11.87 14.19 0.95 0.00 6.71 9.83 6.22 9.54
The Improvement in Recommendation Accuracy of PREEI Compared to the Baseline Models
Effect of the Number of Hidden Layers and Dropout Ratio
模型 MI AIV OP THI VG
RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE
PREEI 0.822 0.587 0.869 0.641 0.836 0.644 0.890 0.642 1.010 0.768
PREEI-A 0.847 0.597 0.870 0.642 0.844 0.659 0.890 0.650 1.012 0.777
PREEI-D 0.877 0.639 0.890 0.686 0.859 0.664 0.898 0.673 1.028 0.803
PREEI-W 0.886 0.675 0.983 0.761 0.919 0.737 1.018 0.785 1.111 0.874
Comparison of Variant Models
用户 评论编号 评论 情感得分&权重
用户1 a For any sound techs that like to make things simple and easy to work，these xlr jumper cables are the way to go，and the quality is impressive. I used them for a new set of wireless mike to jump into my 16 channel board and was it so easy，my mikes are color coded and my cable fitted right in with the same color code thus making it easy to identify from afar and easy to control on the mixing board. 情感得分=1.473
情感权重=0.225
b If you are have problems knowing what and where your systems are terminating，these are a must for you. I have mastched them with my microphones and it is easy for controlling and identifying. 情感得分=-0.070
情感权重=0.048
用户2 a Love my assortment of picks and the price was right. I like that their is different gauges，because I don’t always like using the same so this is great with the three different gauges to choose from in all these nice colours but I only received 47 instead of 48 picks but whose counting，I'm still happy with my picks and would purchase from seller again，thanks. 情感得分=2.768
情感权重=0.505
b Fast shipping，great price and most of all great stings，very satisfied would buy these strings again and would recommend to a friend. 情感得分=0.363
情感权重=0.046
Examples of User Sentiment Weights
[1] 丁浩, 胡广伟, 王婷, 等. 基于时序漂移的潜在因子模型推荐方法[J]. 数据分析与知识发现, 2022, 6(10): 1-8.
[1] (Ding Hao, Hu Guangwei, Wang Ting, et al. Recommendation Method for Potential Factor Model Based on Time Series Drift[J]. Data Analysis and Knowledge Discovery, 2022, 6(10): 1-8.)
[2] 马莹雪, 甘明鑫, 肖克峻. 融合标签和内容信息的矩阵分解推荐方法[J]. 数据分析与知识发现, 2021, 5(5): 71-82.
[2] (Ma Yingxue, Gan Mingxin, Xiao Kejun. A Matrix Factorization Recommendation Method with Tags and Contents[J]. Data Analysis and Knowledge Discovery, 2021, 5(5): 71-82.)
[3] 易明, 刘明, 冯翠翠. 融合异质信息网络表示学习的跨领域推荐研究[J]. 情报学报, 2022, 41(4): 337-349.
[3] (Yi Ming, Liu Ming, Feng Cuicui. Cross-Domain Recommendation Combining Heterogeneous Information Network Representation Learning[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(4): 337-349.)
[4] Liu D H, Li J, Du B, et al. A Hybrid Neural Network Approach to Combine Textual Information and Rating Information for Item Recommendation[J]. Knowledge and Information Systems, 2021, 63(3): 621-646.
doi: 10.1007/s10115-020-01528-2
[5] 李伟卿, 池毛毛, 王伟军. 面向用户长短期偏好调节的可解释个性化推荐方法研究[J]. 图书情报工作, 2021, 65(12): 101-111.
doi: 10.13266/j.issn.0252-3116.2021.12.010
[5] (Li Weiqing, Chi Maomao, Wang Weijun. Explainable Personalized Recommendation Method Based on Adjustment of Users’ Long-and Short-Term Preferences[J]. Library and Information Service, 2021, 65(12): 101-111.)
doi: 10.13266/j.issn.0252-3116.2021.12.010
[6] 聂卉, 邱以菲. 融合用户兴趣及评论效用的评论信息推荐[J]. 图书情报工作, 2021, 65(10): 68-78.
doi: 10.13266/j.issn.0252-3116.2021.10.008
[6] (Nie Hui, Qiu Yifei. Integrating User Interests with Review Helpfulness for Review Recommendation[J]. Library and Information Service, 2021, 65(10): 68-78.)
doi: 10.13266/j.issn.0252-3116.2021.10.008
[7] Mikolov T, Sutskever I, Chen K, et al. Distributed Representations of Words and Phrases and Their Compositionality[C]// Proceedings of the 26th International Conference on Neural Information Processing Systems. 2013.
[8] Pennington J, Socher R, Manning C. GloVe: Global Vectors for Word Representation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014: 1532-1543.
[9] Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. 2019: 4171-4186.
[10] Wang C, Blei D M. Collaborative Topic Modeling for Recommending Scientific Articles[C]// Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011: 448-456.
[11] McAuley J, Leskovec J. Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text[C]// Proceedings of the 7th ACM Conference on Recommender Systems. 2013: 165-172.
[12] Bao Y, Fang H, Zhang J. TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation[C]// Proceedings of the 28th AAAI Conference on Artificial Intelligence. 2014: 2-8.
[13] Tan Y Z, Zhang M, Liu Y Q, et al. Rating-Boosted Latent Topics: Understanding Users and Items with Ratings and Reviews[C]// Proceedings of the 25th International Joint Conference on Artificial Intelligence. 2016: 2640-2646.
[14] Zhang H J, Ji Y Z, Li J X, et al. A Triple Wing Harmonium Model for Movie Recommendation[J]. IEEE Transactions on Industrial Informatics, 2016, 12(1): 231-239.
doi: 10.1109/TII.2015.2475218
[15] Kim D, Park C, Oh J, et al. Convolutional Matrix Factorization for Document Context-Aware Recommendation[C]// Proceedings of the 10th ACM Conference on Recommender Systems. 2016: 233-240.
[16] Salakhutdinov R, Mnih A. Probabilistic Matrix Factorization[C]// Proceedings of the 20th International Conference on Neural Information Processing Systems. 2007: 1257-1264.
[17] Dong X, Yu L, Wu Z H, et al. A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems[C]// Proceedings of the 31st AAAI Conference on Artificial Intelligence. 2017: 1309-1315.
[18] 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. 2017: 425-434.
[19] Li D, Liu H, Zhang Z L, et al. CARM: Confidence-Aware Recommender Model via Review Representation Learning and Historical Rating Behavior in the Online Platforms[J]. Neurocomputing, 2021, 455(C): 283-296.
[20] Shuai J, Zhang K, Wu L, et al. A Review-Aware Graph Contrastive Learning Framework for Recommendation[C]// Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2022: 1283-1293.
[21] Seo S, Huang J, Yang H, et al. Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction[C]// Proceedings of the 11th ACM Conference on Recommender Systems. 2017: 297-305.
[22] Chen C, Zhang M, Liu Y Q, et al. Neural Attentional Rating Regression with Review-Level Explanations[C]// Proceedings of the 2018 World Wide Web Conference. 2018: 1583-1592.
[23] Lu Y C, Dong R H, Smyth B. Coevolutionary Recommendation Model: Mutual Learning Between Ratings and Reviews[C]// Proceedings of the 2018 World Wide Web Conference. 2018: 773-782.
[24] Cho K, van Merrienboer B, Gulcehre C, et al. Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014: 1724-1734.
[25] Koren Y, Bell R, Volinsky C. Matrix Factorization Techniques for Recommender Systems[J]. Computer, 2009, 42(8): 30-37.
[26] Wu L B, Quan C, Li C L, et al. A Context-Aware User-Item Representation Learning for Item Recommendation[J]. ACM Transactions on Information Systems, 2019, 37(2): 22.
[27] Liu H, Zheng C, Li D T C, et al. EDMF: Efficient Deep Matrix Factorization with Review Feature Learning for Industrial Recommender System[J]. IEEE Transactions on Industrial Informatics, 2022, 18(7): 4361-4371.
doi: 10.1109/TII.2021.3128240
[28] Liu Y, Miyazaki J. Knowledge-Aware Attentional Neural Network for Review-Based Movie Recommendation with Explanations[J]. Neural Computing and Applications, 2023, 35(3): 2717-2735.
doi: 10.1007/s00521-022-07689-1
[29] Liu H, Qiao H Z, Shi X Y, et al. Aspect-Aware Asymmetric Representation Learning Network for Review-Based Recommendation[C]// Proceedings of 2022 International Joint Conference on Neural Networks. IEEE, 2022: 1-8.
[30] Forman C, Ghose A, Wiesenfeld B. Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets[J]. Information Systems Research, 2008, 19(3): 291-313.
doi: 10.1287/isre.1080.0193
[31] 彭敏, 席俊杰, 代心媛, 等. 基于情感分析和LDA主题模型的协同过滤推荐算法[J]. 中文信息学报, 2017, 31(2): 194-203.
[31] (Peng Min, Xi Junjie, Dai Xinyuan, et al. Collaborative Filtering Recommendation Based on Sentiment Analysis and LDA Topic Model[J]. Journal of Chinese Information Processing, 2017, 31(2): 194-203.)
[32] Shen R P, Zhang H R, Yu H, et al. Sentiment Based Matrix Factorization with Reliability for Recommendation[J]. Expert Systems with Applications, 2019, 135: 249-258.
doi: 10.1016/j.eswa.2019.06.001
[33] Yang C, Chen X H, Liu L, et al. Leveraging Semantic Features for Recommendation: Sentence-Level Emotion Analysis[J]. Information Processing & Management, 2021, 58(3): 102543.
doi: 10.1016/j.ipm.2021.102543
[34] Cai Y, Ke W M, Cui E, et al. A Deep Recommendation Model of Cross-Grained Sentiments of User Reviews and Ratings[J]. Information Processing & Management, 2022, 59(2): 102842.
doi: 10.1016/j.ipm.2021.102842
[35] 李浩君, 吕韵, 汪旭辉, 等. 融入情感分析的多层交互深度推荐模型研究[J]. 数据分析与知识发现, 2023, 7(3): 43-57.
[35] (Li Haojun, Lv Yun, Wang Xuhui, et al. A Deep Recommendation Model with Multi-Layer Interaction and Sentiment Analysis[J]. Data Analysis and Knowledge Discovery, 2023, 7(3): 43-57.)
[36] Wang P P, Li L, Wang R, et al. Learning Persona-Driven Personalized Sentimental Representation for Review-Based Recommendation[J]. Expert Systems with Applications, 2022, 203: 117317.
doi: 10.1016/j.eswa.2022.117317
[37] Baccianella S, Esuli A, Sebastiani F. SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining[C]// Proceedings of the 7th International Conference on Language Resources and Evaluation. 2010: 2200-2204.
[38] Guo H F, Tang R M, Ye Y M, et al. DeepFM: A Factorization-Machine Based Neural Network for CTR Prediction[C]// Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017: 1725-1731.
[39] Tan J T, Xu S Y, Ge Y Q, et al. Counterfactual Explainable Recommendation[C]// Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021: 1784-1793.
[40] Manjunatha Adithya R, Sandhya K, Sachin G M, et al. Feature Validated Sentiment Recommendation of Hotel Reviews[M]// Soft Computing for Security Applications:Proceedings of ICSCS 2022. Singapore: Springer Nature Singapore, 2022: 413-422.
[41] Kingma D P, Ba J L. Adam: A Method for Stochastic Optimization[C]// Proceedings of the 3rd International Conference on Learning Representations. 2015.
[42] Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: A Simple Way to Prevent Neural Networks from Overfitting[J]. The Journal of Machine Learning Research, 2014, 15(1): 1929-1958.
[1] Li Xuelian, Wang Bi, Li Lixin, Han Dixuan. Sentiment Analysis with Abstract Meaning Representation and Dependency Grammar[J]. 数据分析与知识发现, 2024, 8(1): 55-68.
[2] He Li, Yang Meihua, Liu Luyao. Detecting Events with SPO Semantic and Syntactic Information[J]. 数据分析与知识发现, 2023, 7(9): 114-124.
[3] Han Pu, Gu Liang, Ye Dongyu, Chen Wenqi. Recognizing Chinese Medical Literature Entities Based on Multi-Task and Transfer Learning[J]. 数据分析与知识发现, 2023, 7(9): 136-145.
[4] Xiang Zhuoyuan, Chen Hao, Wang Qian, Li Na. Few-Shot Language Understanding Model for Task-Oriented Dialogues[J]. 数据分析与知识发现, 2023, 7(9): 64-77.
[5] Nie Hui, Cai Ruisheng. Online Doctor Recommendation System with Attention Mechanism[J]. 数据分析与知识发现, 2023, 7(8): 138-148.
[6] Li Guangjian, Yuan Yue. Review of Knowledge Elements Extraction in Scientific Literature Based on Deep Learning[J]. 数据分析与知识发现, 2023, 7(7): 1-17.
[7] Wang Nan, Wang Qi. Evaluating Student Engagement with Deep Learning[J]. 数据分析与知识发现, 2023, 7(6): 123-133.
[8] Wang Xiaofeng, Sun Yujie, Wang Huazhen, Zhang Hengzhang. Construction and Verification of Type-Controllable Question Generation Model Based on Deep Learning and Knowledge Graphs[J]. 数据分析与知识发现, 2023, 7(6): 26-37.
[9] Wu Jialun, Zhang Ruonan, Kang Wulin, Yuan Puwei. Deep Learning Model of Drug Recommendation Based on Patient Similarity Analysis[J]. 数据分析与知识发现, 2023, 7(6): 148-160.
[10] Liu Yang, Zhang Wen, Hu Yi, Mao Jin, Huang Fei. Hotel Stock Prediction Based on Multimodal Deep Learning[J]. 数据分析与知识发现, 2023, 7(5): 21-32.
[11] Huang Xuejian, Ma Tinghuai, Wang Gensheng. Detecting Weibo Rumors Based on Hierarchical Semantic Feature Learning Model[J]. 数据分析与知识发现, 2023, 7(5): 81-91.
[12] Yan Shangyi, Wang Jingya, Liu Xiaowen, Cui Yumeng, Tao Zhizhong, Zhang Xiaofan. Microblog Sentiment Analysis with Multi-Head Self-Attention Pooling and Multi-Granularity Feature Interaction Fusion[J]. 数据分析与知识发现, 2023, 7(4): 32-45.
[13] Zhang Yu, Zhang Haijun, Liu Yaqing, Liang Kejin, Wang Yueyang. Multimodal Sentiment Analysis Based on Bidirectional Mask Attention Mechanism[J]. 数据分析与知识发现, 2023, 7(4): 46-55.
[14] Han Pu, Zhong Yule, Lu Haojie, Ma Shiwen. Identifying Named Entities of Adverse Drug Reaction with Adversarial Transfer Learning[J]. 数据分析与知识发现, 2023, 7(3): 131-141.
[15] Li Haojun, Lv Yun, Wang Xuhui, Huang Jieya. A Deep Recommendation Model with Multi-Layer Interaction and Sentiment Analysis[J]. 数据分析与知识发现, 2023, 7(3): 43-57.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626，82624938   E-mail:jishu@mail.las.ac.cn