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A session-based recommendation system is designed to predict the user's next click behavior based on an ongoing session. Existing session-based recommendation systems usually model a session into a sequence and extract sequence features through recurrent neural network. Although the performance is greatly improved, these procedures ignore the relationships between items that contain rich information. In order to obtain rich items embeddings, we propose a novel Recommendation Model based ondoi:10.1155/2021/6661901 fatcat:5m6hln2znvfsfh5holnny5kbvy