From Head to Tail: Asymmetric Knowledge Transfer in Long-tail Recommendation with Generative Semantic IDs
Researchers have developed a new framework called AKT-Rec to address challenges in long-tail recommendation systems, particularly those in e-commerce platforms with significant data imbalance. This framework utilizes multimodal large language models (MLLMs) to generate semantic IDs that align content features with collaborative filtering signals. AKT-Rec incorporates an asymmetric contrastive objective and an activity-aware gating mechanism to facilitate knowledge transfer from head to tail items, improving representation learning. Experiments on a large-scale industrial dataset and subsequent online A/B testing on Alibaba's Tmall platform demonstrated substantial improvements in key metrics such as AUC, GAUC, CTR, and GMV. AI
IMPACT Enhances e-commerce recommendation systems by improving CTR and GMV through better handling of data imbalance.