Abstract
For the session recommendation scenario, user behaviors in the same session are intrinsically related, and the context information of user session behavior is introduced into the session, and the behavior in the session is modeled. Introducing a dual attention mechanism, assigning different weights to user input behavior data, constructing a model, and conducting experiments on two public datasets. Compared with the benchmark model, this model has improved in various evaluation indices, proving its effectiveness.
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Acknowledgements
This work was supported in part by Natural Research Science Institute of Anhui Provincial Department of Education(2022AH051379); School level teaching and research project (szxy2022jyxm40); The Ministry of Education's Industry School Cooperation Collaborative Education Project (202102076023); Suzhou University School Level Quality Engineering Project(szxy2023jyjf82); Natural Research Science Institute of Anhui Provincial Department of Education(KJ2021A1110); Suzhou University Doctoral Research Initiation Fund Project (2023BSK023).
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Zhang, X., Liu, W., Zhou, W. (2024). Conversational Recommendation Based on Graph Neural Network Model with Dual Attention Mechanism. In: Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2023. Lecture Notes in Electrical Engineering, vol 1033. Springer, Singapore. https://doi.org/10.1007/978-981-99-7502-0_29
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DOI: https://doi.org/10.1007/978-981-99-7502-0_29
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