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G3SR: Global Graph Guided Session-Based Recommendation | IEEE Journals & Magazine | IEEE Xplore

G3SR: Global Graph Guided Session-Based Recommendation


Abstract:

Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendations under the condition that user profiles and the complete h...Show More

Abstract:

Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendations under the condition that user profiles and the complete historical behavioral data of a target user are unavailable. Previous works consider each session individually and try to capture user interests within a session. Despite their encouraging results, these models can only perceive intra-session items and cannot draw upon the massive historical relational information. To solve this problem, we propose a novel method named global graph guided session-based recommendation (G3SR). G3SR decomposes the session-based recommendation workflow into two steps. First, a global graph is built upon all session data, from which the global item representations are learned in an unsupervised manner. Then, these representations are refined on session graphs under the graph networks, and a readout function is used to generate session representations for each session. Extensive experiments on two real-world benchmark datasets show remarkable and consistent improvements of the G3SR method over the state-of-the-art methods, especially for cold items.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 34, Issue: 12, December 2023)
Page(s): 9671 - 9684
Date of Publication: 24 March 2022

ISSN Information:

PubMed ID: 35324448

Funding Agency:


References

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