The hybrid sequential recommender system synthesis method based on attention mechanism with automatic knowledge graph construction
DOI:
https://doi.org/10.20535/SRIT.2308-8893.2025.1.01Keywords:
recommender system, graph neural network, graph embeddingsAbstract
Sequential personalized recommendations, such as next best offer prediction or modeling demand evolution for next basket prediction, remain a key challenge for businesses. In recent years, deep learning models have been applied to solve these problems and demonstrated high feasibility. With the introduction of graph-based deep learning, it has become easier to perform collaborative filtering and link prediction tasks. The current paper proposes a new method of building a recommender system using a graph representation learning framework in combination with deep neural networks for sequence-to-sequence modeling and statistical learning for sequence-to-graph mapping. Benchmarking model performance on an online retail store visits dataset provides evidence of the method’s ranking capabilities.
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