Research on recommendation algorithm based on neural network fusing user behavior sequence

Zengguang Wang; Deyong Wang

doi:10.1117/12.2671253

28 April 2023 Research on recommendation algorithm based on neural network fusing user behavior sequence

Zengguang Wang, Deyong Wang

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 1261057 (2023) https://doi.org/10.1117/12.2671253
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

The traditional neural network collaborative filtering algorithm is developed from the matrix decomposition algorithm, and compared with the matrix decomposition, the neural network collaborative filtering algorithm uses a multi-layer perceptron to replace the original dot product operation. Although this method can fully cross user vectors and item vectors, it cannot learn the timing problems on the user side. To solve this, a collaborative filtering model based on deep neural network fusion user sequence is proposed. The model still models the interaction information between the user and the object, with the difference being that recurrent neural networks are introduced when modeling the user. The probability of the user's interest in the item is calculated from the obtained user characteristics and item characteristics. Experiments on the MovieLens and MIND datasets show that the proposed model is higher than the matrix decomposition algorithm and neural network co-filtering algorithm on the AUC and F1 Score indicators, which verifies the accuracy of the model's recommendation effect.

Citation Download Citation

Zengguang Wang and Deyong Wang "Research on recommendation algorithm based on neural network fusing user behavior sequence", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 1261057 (28 April 2023); https://doi.org/10.1117/12.2671253

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