Knowledge-Guided Adaptive Sequence Reinforcement Learning Model

The sequence recommendation can be formalized as a Markov decision process and then transformed into a deep reinforcement learning problem. Mining critical information from user sequences is a key step, such as preference drift and dependencies between sequences. In most current deep reinforcement learning recommendation systems, a fixed sequence length is taken as the input. Inspired by knowledge graphs, a knowledge-guided adaptive sequence reinforcement learning model is proposed. Firstly, using the entity relationship of the knowledge graph, a partial sequence is intercepted from the complete user feedback sequence as a drift sequence. The item set in the drift sequence represents the user′s current preference, and the sequence length represents the user′s preference change speed. Then, a gated recurrent unit is utilized to extract the user′s preference changes and dependencies between items, while the self-attention mechanism selectively focuses on key item information. Finally, a compound reward function is designed, including discount sequence rewards and knowledge graph rewards, to alleviate the problem of sparse reward. Experiments on four real-world datasets demonstrate that the proposed model achieves superior recommendation accuracy. © 2023 Journal of Pattern Recognition and Artificial Intelligence. All rights reserved.