Year: 2022
Type: preprint
Abstract: Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendation 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 G$^3$SR (Global Graph Guided Session-based Recommendation). G$^3$SR 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 G$^3$SR method over the state-of-the-art methods, especially for cold items.
Source: arXiv (Cornell University)
Authors: Deng, Zhi-Hong, Wang, Chang-Dong, Huang, Ling, Lai, Jian-Huang, Yu, Philip S.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1200806771, G1797038515, III: Small: Exploiting the Massive User Generated Utterances for Intent Mining under Scarce Annotations, III: Medium: Collaborative Research: An Extensible Heterogeneous Network Embedding Framework with Application Specific Adaptation, III: Medium: Collaborative Research: Self-Supervised Recommender System Learning with Application Specific Adaption, G4442155276, G4815838450, G5409299611, G544703200, G6018787573, G6898366254, G7502447764, SaTC: CORE: Small: Collaborative: Learning Dynamic and Robust Defenses Against Co-Adaptive Spammers
OpenAlex ID: W4221149633