A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Factorial User Modeling with Hierarchical Graph Neural Network for Enhanced Sequential Recommendation
[article]
2022
arXiv
pre-print
Most sequential recommendation (SR) systems employing graph neural networks (GNNs) only model a user's interaction sequence as a flat graph without hierarchy, overlooking diverse factors in the user's preference. Moreover, the timespan between interacted items is not sufficiently utilized by previous models, restricting SR performance gains. To address these problems, we propose a novel SR system employing a hierarchical graph neural network (HGNN) to model factorial user preferences.
arXiv:2207.13262v1
fatcat:cyzjfqi6mfcmpck5m2a7a2y5u4