An improved dual intent graph neural network for session-based new item recommendation

Hongjun Liu; Wenming Cao; Xujun Yang

doi:10.1117/12.3035107

19 July 2024 An improved dual intent graph neural network for session-based new item recommendation

Hongjun Liu, Wenming Cao, Xujun Yang

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132133P (2024) https://doi.org/10.1117/12.3035107
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

The role of session-based recommendation in daily life is essential. There has been a focus on utilizing graph neural networks (GNNs) to recommend new items in the session contexts. However, existing methods only consider item transitions within individual sessions, posing challenges for GNNs in learning item embeddings and user preferences. To investigate the users’ intent and enhance the users’ satisfaction with new recommended items simultaneously, this paper proposes a novel dual-intent graph neural network approach (DGNN). Specifically, DGNN shall first incorporates not only pairwise item transition information within individual sessions but also the global information from the entire data set when adopting GNNs to learn item embeddings. Second, with item embeddings and items’ taxonomy tree embeddings fed into the dual-intent nets layer, DGNN synthesizes users’ intent by leveraging item information from the perspectives of both soft-attention and data distribution. Third, this paper has designed a novel method to infer the embeddings of new items. Experimental results on several data sets have verified the effectiveness of the proposed DGNN.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hongjun Liu, Wenming Cao, and Xujun Yang "An improved dual intent graph neural network for session-based new item recommendation", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132133P (19 July 2024); https://doi.org/10.1117/12.3035107

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