Volume 32 Issue 4
Jul.  2023
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NAN Jiangang, WANG Yajun, WANG Chengcheng, “Rating Prediction Model Based on Causal Inference Debiasing Method in Recommendation,” Chinese Journal of Electronics, vol. 32, no. 4, pp. 932-940, 2023, doi: 10.23919/cje.2022.00.076
Citation: NAN Jiangang, WANG Yajun, WANG Chengcheng, “Rating Prediction Model Based on Causal Inference Debiasing Method in Recommendation,” Chinese Journal of Electronics, vol. 32, no. 4, pp. 932-940, 2023, doi: 10.23919/cje.2022.00.076

Rating Prediction Model Based on Causal Inference Debiasing Method in Recommendation

doi: 10.23919/cje.2022.00.076
Funds:  This work was supported by the National Natural Science Foundation of China (61503169, 61802161) and the Natural Science Foundation of Liaoning Province (2020-MS-291)
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  • Author Bio:

    Jiangang NAN was born in Shandong Province, China, in 1995. He received the B.E. degree from School of Artificial Intelligence, Shandong Management University, in 2020. He is now an M.E. candidate in the School of Electronic & Information Engineering, Liaoning University of Technology. His research interests include recommended system and natural language processing. (Email: nanjiangang@gmail.com)

    Yajun WANG (corresponding author) was born in Liaoning Province, China, in 1978. She received the B.S. and M.S. degrees in electronics information engineering from Shenyang Normal University in 2001 and 2004 respectively. She received the Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2015. From 2004 to 2019, she was a teacher with Liaoning University and Technology, Jinzhou, China. Since 2020, she has been a Professor. She is the author of more than 30 articles. Her research interests include multivariate statistical modeling, process monitoring, and data mining and big data analysis. (Email: wyjjs2022@163.com)

    Chengcheng WANG was born in Shandong Province, China, in 1998. He received the B.E. degree from School of Physics and Physical Engineering, Qufu Normal University, in 2020. He is now an M.E. candidate in the School of Electronic & Information Engineering, Liaoning University of Technology. His research interests include recommended system and natural language processing. (Email: 3502883354@qq.com)

  • Received Date: 2022-04-07
  • Accepted Date: 2022-09-26
  • Available Online: 2022-11-14
  • Publish Date: 2023-07-05
  • The rating prediction task plays an important role in the recommendation model. Most existing methods predict ratings by extracting user and items characteristics from historical review data. However, the recommended strategies in historical review data are often based on partial observational data, which having the problems of unbalanced distribution, lack of robustness, and inability to obtain unbiased prediction results. Therefore, a novel rating prediction model based on causal inference debiasing (CID) method is proposed. The model can mitigate the negative effects of context bias and improve the robustness by studying the causal relationship between review information and user ratings. The proposed CID rating prediction model is plug-and-play and is not limited to one baseline prediction method. The proposed method is tested on four open datasets. The results show that the proposed method is feasible. Compared with the most advanced models, the prediction accuracy of the CID rating prediction model has been further improved. The experimental results show the debiasing effectiveness of the CID rating prediction model.
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