Xiyao Ma

Beyond accuracy, diversity has become a crucial factor to evaluate a recommendation system as higher diversity helps mitigate echo chamber issue and improve user satisfaction. Recently, great success has been made to improve diversity, but the approaches often sacrifice much lower accuracy. Herein this work, we propose contrastive co-training for diversified recommendation that improves diversity greatly and achieves comparable or even better accuracy.

Large Language models (LLMs) have demonstrated impressive in-context learning (ICL) capabilities, where a LLM makes predictions for a given test input together with a few input-output pairs (demonstrations). Nevertheless, the inclusion of demonstrations leads to a quadratic increase in the computational overhead of the self-attention mechanism. Existing solutions attempt to distill lengthy demonstrations into compact vectors.