Graph attentive transfer learning for few-shot session-based recommendation

Ailing Qi; Chen Zhang

doi:10.1117/12.2637163

15 June 2022 Graph attentive transfer learning for few-shot session-based recommendation

Ailing Qi, Chen Zhang

Proceedings Volume 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022); 122850Y (2022) https://doi.org/10.1117/12.2637163
Event: International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 2022, Zhuhai, China

Abstract

The session-based recommendation predicts the user’s next-click preference with anonymous temporal sessions, which meets the considerable challenges in scarce item recommendation or the newly-established e-commerce platform. Those practical limitations severely constrain the performance of existing session-based recommendation models, which is called by few-shot session-based recommendation task. This paper proposes a Graph Attentive Transfer Learning (GATL) approach involving a source domain with sufficient data to distill useful knowledge into the target domain with limited sessions. Concretely, GATL contains an intra-session attentive feature learning module to explore the correlations among different items in each session and a cross-domain inter-session interactive feature learning with an adversarial transfer learning strategy to solve the few-shot learning in target session-based recommendation. The proposed modules ensure GATL can extract the intra- and inter-session graph feature vectors and feed them into an improved prediction layer for overall item prediction. Experimental results on two datasets (Diginetica and Retailrocket) demonstrate the effectiveness of our proposed GATL model on the few-shot session-based recommendation task.

Citation Download Citation

Ailing Qi and Chen Zhang "Graph attentive transfer learning for few-shot session-based recommendation", Proc. SPIE 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 122850Y (15 June 2022); https://doi.org/10.1117/12.2637163

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KEYWORDS

Performance modeling

Data modeling

Detection and tracking algorithms

Convolution

Mining

Neural networks

Data integration

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