Recommendation Model Combining Deep Sentiment Analysis and Scoring Matrix

LI Shuzhi; YU Letao; DENG Xiaohong

doi:10.11999/JEIT200779

Volume 44 Issue 1

Jan. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(1): 245-253

LI Shuzhi, YU Letao, DENG Xiaohong. Recommendation Model Combining Deep Sentiment Analysis and Scoring Matrix[J]. Journal of Electronics & Information Technology, 2022, 44(1): 245-253. doi: 10.11999/JEIT200779

Citation:

LI Shuzhi, YU Letao, DENG Xiaohong. Recommendation Model Combining Deep Sentiment Analysis and Scoring Matrix[J]. Journal of Electronics & Information Technology, 2022, 44(1): 245-253. doi: 10.11999/JEIT200779

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Recommendation Model Combining Deep Sentiment Analysis and Scoring Matrix

doi: 10.11999/JEIT200779

1.
School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
2.
College of Applied Science, Jiangxi University of Science and Technology, Ganzhou 341000, China

Funds: The National Natural Science Foundation of China (61762046), The Science and Technology Research Project of Education Department of Jiangxi Province (GJJ181505)

Received Date: 2020-09-02
Rev Recd Date: 2021-04-09

Available Online: 2021-07-13

Publish Date: 2022-01-10

Abstract

Abstract

Most recommendation systems have a data sparsity problem, which limits the validity of the model that they use. However, the user’s comments on a commodity contain a lot of information. Emotional analysis of the comment text and the extraction of key factors for model learning can effectively alleviate the data sparsity problem, but only the use of comment data and ignore the main factors of the scoring data will affect the recommendation accuracy. To improve further the precision of recommendations, a deep model for the processing of Review Texts and Rating Matrices (RTRM) is proposed. The model extracts deep-level features and combines them to make rating predictions. Then, by using the pre-trained Electra model, the implicit expression of each comment is get, and combining with the deep emotion analysis and attention mechanism, the analysis of the comment text is realized from the context semantic level. It solves the problem that it is difficult to analyze the semantics of short text; User (item) reviews interact with a rating matrix to predict the user’s rating of a product in the fusion layer module. Finally, the Mean Square Error (MSE) is used to perform performance comparison experiments on 6 sets of data sets. Experimental results show that the performance of the proposed model outperforms significantly other systems on a variety of datasets, and the average prediction error is reduced by a maximum of 12.821%, the model is suitable for recommending accurate items to users.
- Recommendation system,
- Matrix decomposition,
- Review text,
- Scoring data,
- Deep learning

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References(17)

References

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