Jiaqi Cui

This work proposes UniRec, a Unified Cross-stage Recommendation Fusion model, and introduces a dual-axis preference alignment objective, and finds that unconstrained end-to-end fusion optimization can exploit imbalances in item attribute distributions, over-concentrating on high-reward regions at the cost of other objectives.

KOAL's innovative use of clinical context and expert knowledge allows for a more nuanced and accurate prediction of prostate cancer grading, outperforming traditional methods.