Session-Enhanced Graph Neural Network Recommendation Model (SE-GNNRM)
Source abstract
Abstract
Session-based recommendation aims to predict anonymous user actions. Many existing session recommendation models do not fully consider the impact of similar sessions on recommendation performance. Graph neural networks can better capture the conversion relationship of items within a session, but some intra-session conversion relationships are not conducive to recommendation, which requires model learning more representative session embeddings. To solve these problems, an improved session-enhanced graph neural network recommendation model, namely SE-GNNRM, is proposed in this paper. In our model, the complex transitions relationship of items and more representative item features are captured through graph neural network and self-attention mechanism in the encoding stage. Then, the attention mechanism is employed to combine short-term and long-term preferences to construct a global session graph and capture similar session information by using a graph attention network fused with similarity. In order to prove the effectiveness of the constructed SE-GNNRM model, three public data sets are selected here. The experiment results show that the SE-GNNRM outperforms the existing baseline models and is an effective model for session-based recommendation.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Recommender Systems and Techniques · Advanced Graph Neural Networks · Machine Learning in Healthcare
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Software, Dalian Jiaotong University, Dalian 116028, China
Funding
National Natural Science Foundation of China
References (42)
Show source-provided references
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