E-commerce recommender system based on sentiment multi-label classification

Dun Ao; Cong Zhang

doi:10.1117/12.3032845

5 July 2024 E-commerce recommender system based on sentiment multi-label classification

Dun Ao, Cong Zhang

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131845H (2024) https://doi.org/10.1117/12.3032845
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Text sentiment analysis and its incorporation into recommender systems have been study topics in the past few years. Recommendation accuracy can be increased by using sentiment elements. One of the most popular is dichotomous sentiment analysis. Nevertheless, the real emotions of users are diverse, and sentiment dichotomous classification is not sufficient to fully express user attitudes. To address this problem, this paper innovatively proposes the Sentiment Multilabel Classification Recommender System (SMCRS) model, which establishes a six-label sentiment classification module for text, and extracts relevant word features for each label by decomposing the sentence The label prediction is finally realized. Moreover, the model builds higher-order interactions between various features to realize the recommendation function by incorporating sentiment as well as user and item information. Finally, we tested the performance of SMCRS on the JD dataset, and the accuracy of the sentiment classification module is as high as 78.02%, while the AUC of the whole model is improved by 6.35%. This is a great improvement, and it also proves that sentiment multi-classification is very helpful for the performance improvement of recommender systems.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dun Ao and Cong Zhang "E-commerce recommender system based on sentiment multi-label classification", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131845H (5 July 2024); https://doi.org/10.1117/12.3032845

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