Can I read Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks on EtoBox?

Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks by Ruihong Qiu; Zi Huang; Jingjing Li; Hongzhi Yin is a Computer Science article available to read on EtoBox.

What is Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks about?

Different from the traditional recommender system, the session-based recommender system introduces the concept of the session , i.e., a sequence of interactions between a user and multiple items within a period, to preserve the user’s recent interest. The existing work on the session-based recommender system mainly relies on mining sequential patterns within individual sessions, which are not expressive enough to capture more complicated dependency relationships among items. In addition, it does not consider the cross-session information due to the anonymity of the session data, where the linkage between different sessions is prevented. In this article, we solve these problems with the graph neural networks technique. First, each session is represented as a graph rather than a linear sequence structure, based on which a novel F ull G raph N eural N etwork (FGNN) is proposed to learn complicated item dependency. To exploit and incorporate cross-session information in the individual session’s representation learning, we further construct a B roadly C onnected S ession (BCS) graph to link different sessions and a novel Mask-Readout function to improve session embedding based on the BC

Who reads Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks?

Author

Ruihong Qiu; Zi Huang; Jingjing Li; Hongzhi Yin

Publisher

Association for Computing Machinery (ACM)

Published

2020

Language

EN

Field

Computer Science (Physical Sciences)