Гiбридний метод побудови секвенцiальних рекомендацiйних систем, заснований на автоматичному синтезi графiв знань та механiзмi уваги
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 intr...
Збережено в:
| Дата: | 2025 |
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| Автори: | , |
| Формат: | Стаття |
| Мова: | Англійська |
| Опубліковано: |
The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"
2025
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| Теми: | |
| Онлайн доступ: | http://journal.iasa.kpi.ua/article/view/329313 |
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| Назва журналу: | System research and information technologies |
Репозитарії
System research and information technologies| Резюме: | 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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