MICRecBox是由吉林大学移动智能计算实验室数据挖掘组（Mobile Intelligent Computing Lab-Data Mining, MIC-DM）构建的开源可拓展推荐系统代码库，致力于跟踪推荐系统研究前沿和推动推荐系统领域开源发展。MICRecBox秉承组内VED原则（V-valuable, E-end2end, D-details）,关注开源库代码的实用性和有效性，实现从数据处理、训练方法、评价指标、SOTA模型全覆盖的端到端实现，并提供丰富的用例支持。目前MICRecBox主要包括序列推荐（Sequential Recommendation）和去偏推荐（Debiasing Recommendation）两个方向，主要由姜毅恒博士和许航同硕士进行维护。
： "Spatial-Temporal Interval Aware Individual Future Trajectory Prediction" IEEE Transactions on Knowledge and Data Engineering 2023 (TKDE)
： "Spatial-Temporal Interval Aware Sequential POI Recommendation," 2022 IEEE 38th International Conference on Data Engineering (ICDE)
："Revenge of MLP in Sequential Recommendation"
："Causal Structure Representation Learning of Confounders in Latent Space for Recommendation."
："DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops"
："Separating and Learning Latent Confounders to Enhancing User Preferences Modeling"
