Personalization and retrieval

Recommender Systems

We study sequential 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

5 works

MetaSR

The official implementation of a meta-learning framework that bridges next-item prediction and masked-language modeling for recommendation.

MMPCBench

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.

IISAN-Versa

A decoupled parameter-efficient adaptation framework for symmetric and asymmetric multimodal foundation models in recommendation.

SOLAR

An open implementation for aligning language-model recommenders with both relevance and serendipity objectives.

Publications

9 papers