Dr. ZhaoLi Zhang Profile

Dr. ZhaoLi Zhang

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Proceedings Article | 30 November 2022 Paper

A click-through rate prediction model incorporating user review text and behavior sequence

Wei Zhang, ZhaoBin Kang, YaHui Han, BaoLin Yi, ZhaoLi Zhang

Proceedings Volume 12456, 124561P (2022) https://doi.org/10.1117/12.2659364

KEYWORDS: Data modeling, Performance modeling, Feature extraction, Neural networks, Machine learning

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There is a high correlation between user behavior and user features in recommender systems. User review texts reflect user preferences and item feature information. However, the current research on CTR prediction models based on user behaviors fails to fully mine user features. As a result, the prediction accuracy of the model is not high. To solve this problem, we propose a click-through rate prediction model that fuses user comment text and behavior sequence. The model uses a text convolutional neural network to extract the features of user review text to obtain the feature vector of user comment text, and uses an attention mechanism to capture the user's interest points from the user's behavior sequence to obtain the user's interest feature vector. A multi-layer perceptron is then used to fuse the user's comment text feature vector, interest feature vector and item feature vector for click-through rate prediction. The experimental results show that the proposed model has better prediction performance than current click-through rate prediction models.

Proceedings Article | 7 March 2022 Paper

Self-attention perceptron personalized recommendation model with automatic feature correlation

Wei Zhang, Yahui Han, Xiaoxuan Shen, Baolin Yi, Zhaoli Zhang

Proceedings Volume 12167, 1216718 (2022) https://doi.org/10.1117/12.2628566

KEYWORDS: Performance modeling, Neural networks, Feature extraction, Matrices, Multilayers

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How to automatically obtain cross-features with different weight values is a significant issue in the research of recommendation models. Traditional recommendation models cannot automatically learn the deep-level features of users and items to obtain cross-features. The mixed processing of dense numerical features and sparse categorical features will result in more information loss during dimensionality reduction. Cross features occupy the same weight in the recommendation process, which will lead to the non-prominence of critical features and reduce the accuracy of model recommendations. This paper proposes a personalized recommendation model (MSRN) for self-attention perceptron with automatic feature correlation. The model first processes the numerical features and category features in double towers to reduce the loss of feature information. Numerical cross-feature matrix and category cross-feature matrix use multilayer perceptrons to automatically mine the hidden knowledge and relationships between features. The model uses the Hadamard product to process it to obtain the cross feature matrix and uses the self-attention mechanism to assign different weights to the extracted cross-features. The experimental results on the public data set show that the recommended evaluation indicators of this model, MAE, and RMSE, are better than the current advanced recommendation models and have better accuracy and stability.

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