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A Graph Positional Attention Network for Session-Based Recommendation
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AI
DOI：10.1109/ACCESS.2023.3235353.png)
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Abstract
En 中文
The main idea of a session-based recommendation system is to model the user's historical click sequence and then summarize user preferences and predict the items the user will interact with. The session recommendation model based on graph neural networks has attracted much attention in recent years because it can accurately obtain the local relationship between items. However, the traditional session recommendation model based on graph neural Networks lack the use of user's higher-order features or fail to address the impact of item position information on the current session, which are both critical to the recommendation system. In addition, some models proposed the position information while neglects the click frequency information. We propose a graph network recommendation model called GPAN based on position attention in response to the abovementioned problems. Specifically, we propose a novel high-low order session perceptron that uses the perceptron to model undirected and directed graphs separately to obtain high and low order item representations in a session. For position information, we designed a position layer to calculate independently. Finally, the user's short-term preference and long-term preference are aggregated to obtain the recommendation sequence. The results through a large number of experiments on three real datasets show that the performance of the proposed GPAN model is the best.
Keywords:
Predictive models
Markov processes
Sparse matrices
Recommender systems
Directed graphs
Deep learning
Matrix decomposition
Graph neural network
session-based recommendations
attention network
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3.6
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9.8W
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29.4W
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