sequential-recommendation

This paper proposes GenCDSR, a generative framework for cross-domain sequential recommendation with hybrid tokenization and serial-parallel decoding, achieving improved accuracy and significantly reduced inference latency compared to state-of-the-art baselines.

ClockRoPE introduces random Fourier rotations to model temporal periodicity in sequential recommendation, theoretically grounded and validated via online A/B tests at a major video-sharing platform.

Proposes a multimodal framework integrating audio, lyric, and semantic signals with LLM-based sequential reasoning for session-based music recommendation, achieving up to 95% recall improvement over ID-only baselines.

The paper introduces the Bayesian Filtering Transformer (BFT), which incorporates uncertainty into Transformers via precision-weighted attention and Kalman update residuals, improving performance on sequential recommendation and noisy LLM fine-tuning.