MetaSR
The official implementation of a meta-learning framework that bridges next-item prediction and masked-language modeling for recommendation.
Personalization and retrieval
Recommender Systems
We study sequential, conversational, and multimodal recommendation: how models represent user intent, adapt foundation models efficiently, preserve useful cross-modal signals, and balance relevance with discovery. This area connects peer-reviewed papers to the official implementations and reproducibility resources maintained by their authors.
Projects and implementations
8 worksMMPCBench
A benchmark for measuring how multimodal language models reconstruct missing product text or imagery and support recommendation.
FDRec
Frequency-decoupled knowledge distillation for reducing multimodal recommendation cost while preserving useful cross-modal signals.
Guider
The official implementation of a guided-calibration framework for denoising multimodal recommender systems.
IISAN-Versa
A decoupled parameter-efficient adaptation framework for symmetric and asymmetric multimodal foundation models in recommendation.
SAPIENT
A conversational recommendation system that combines a learned agent with Monte Carlo tree search for multi-turn planning.
SOLAR
An open implementation for aligning language-model recommenders with both relevance and serendipity objectives.
TARec
A teacher-assisted Wasserstein distillation pipeline for compressing multimodal recommenders into efficient ID-based student models.
Publications
14 papersBridging NIP and MLM: A Unified Meta-Learning Framework for Sequential Recommendation
Y Li, E Ni, Y Liu, et al.
Bridging the Gap: Teacher-Assisted Wasserstein Knowledge Distillation for Efficient Multi-Modal Recommendation
Z Zhuang, H Du, H Han, et al.
Crossan: Towards Efficient and Effective Adaptation of Multiple Multimodal Foundation Models for Sequential Recommendation
J Fu, Y Ni, JM Jose, et al.
Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation
J Fu, X Ge, X Xin, et al.
Frequency-Decoupled Distillation for Efficient Multimodal Recommendation
Z Zhuang, H Li, J Fu, et al.
Mitigating Data Sparsity with Self-Supervised Contrastive Diffusion for Mamba-based Sequential Recommendation
Jiayang Wang, Yihan Liu, Hongji Li, et al.
SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree Search
H Du, B Peng, X Ning
SOLAR: Serendipity Optimized Language Model Aligned for Recommendation
Z Yuan, L Sun, Y Zhuang, et al.
Teach Me How to Denoise: A Universal Framework for Denoising Multi-Modal Recommender Systems via Guided Calibration
H Li, H Du, Y Li, et al.
An Empirical Study of Training ID-Agnostic Multi-Modal Sequential Recommenders
Y Li, H Du, Y Ni, et al.
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation
H Du, H Yuan, P Zhao, et al.
Feature-Aware Contrastive Learning with Bidirectional Transformers for Sequential Recommendation
H Du, H Yuan, P Zhao, et al.
Sequential Recommendation with Diffusion Models
H Du, H Yuan, Z Huang, et al.
Contrastive Learning with Bidirectional Transformers for Sequential Recommendation
H Du, H Shi, P Zhao, et al.