Movie Recommendation System Using Composite Ranking

Aashal, Kamdar,; Mehta, Irish

doi:10.1007/978-3-031-35078-8_39

Cited by 1 publication

(1 citation statement)

References 28 publications

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“…With the continuous advancement of artificial intelligence technology and the rapid development of internet technology, text classification [2] algorithms based on deep learning methods have been proposed. Deep learning-based text classification algorithms avoid the limitations of manually designed rules and provide higher-dimensional and more effective representations for text mining and text vectorization [3] .…”

Section: Introductionmentioning

confidence: 99%

Research on Chinese Spam SMS Classification Based on ECA-TextCNN Model

Ren

2024

Frontiers in Artificial Intelligence and Applications

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In response to the issues of traditional Chinese spam SMS classification models, which fail to comprehensively extract text features and obtain representative text features, we propose a text classification model based on the ECA-TextCNN model, combining an efficient channel attention mechanism and TextCNN classification model. Firstly, the model utilizes a skip-gram pre-training model for text representation. Secondly, it employs convolutional kernels of different sizes for feature extraction. Furthermore, ECA-Net is used to assign corresponding weights to each channel feature. Finally, the feature vectors are inputted into the softmax layer to obtain the classification results. The experiments are conducted on the publicly available Message80W Chinese SMS dataset. The results show that compared to the baseline model, the text classification model based on ECA-TextCNN exhibits varying degrees of improvement in classification accuracy, precision, recall, and F1 value.

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Section: Introductionmentioning

confidence: 99%

Research on Chinese Spam SMS Classification Based on ECA-TextCNN Model

Ren

2024

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

show abstract

Movie Recommendation System Using Composite Ranking

Cited by 1 publication

References 28 publications

Research on Chinese Spam SMS Classification Based on ECA-TextCNN Model

Research on Chinese Spam SMS Classification Based on ECA-TextCNN Model

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