Sequential augmented attention graph neural network for session-oriented recommendation

Xingchen Du; Shanhong Zheng; Qi Zhan; Guochun Wang

doi:10.1117/12.3011839

7 December 2023 Sequential augmented attention graph neural network for session-oriented recommendation

Xingchen Du, Shanhong Zheng, Qi Zhan, Guochun Wang

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129413D (2023) https://doi.org/10.1117/12.3011839
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

In order to solve the problem that existing session recommendation methods ignore the temporal relationship between items and do not consider the correlation between the items of interest and all items, which leads to poor recommendation results. A novel session recommendation model based on Graph Neural Network (GNN), Long- and Short-Term Memory network (LSTM) and attention mechanism is proposed. The GNN and LSTM are used to capture the complex dependencies and temporal relationships between items to obtain item embeddings; the global embeddings are generated by combining multi-headed attention and soft attention to accurately represent global preferences; the interest attention mechanism is introduced to generate interest embeddings to activate interest item relevance; finally, the current embeddings are combined to obtain the final session representation and predict the next click. The model is compared with the existing benchmark model on three public datasets and the results are all improved, which proves that the proposed method is very effective.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xingchen Du, Shanhong Zheng, Qi Zhan, and Guochun Wang "Sequential augmented attention graph neural network for session-oriented recommendation", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129413D (7 December 2023); https://doi.org/10.1117/12.3011839

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