Generative recommendation improved for diverse item discovery and sales growth
Diverse-Teacher On-Policy Distillation for Generative Recommendation
Information Retrieval
Summary
Recommendation systems often suggest popular items but struggle to show less popular ones, limiting diversity. The authors propose HERO, a method that trains separate teachers for popular and unpopular items to guide the recommendation model better. This approach helps the system learn from a wider variety of items, increasing recommendation diversity. Their tests on several datasets and an online shopping platform show improvements in both recommendation variety and sales.
What this means in practice
- •For ecommerce platform teams: Enhance product recommendation diversity and increase sales by implementing HERO’s dual-teacher distillation approach to better balance popular and unpopular item suggestions.$Commercial implications: This paper enables improved recommendation algorithms that boost online retail sales by increasing recommendation diversity, directly benefiting ecommerce platforms.
- •For recommendation system engineers: Improve training efficiency and recommendation quality by using diverse-teacher on-policy distillation to reduce popularity bias in generative recommendation models.
Authors
Zhipeng Yi, Xinyu Lin, Minxuan Shan, Zipeng Chen, Yujiao Zhao, Chuanyu Xu, Wenjie Wang
Abstract
Generative recommendation generates the semantic ID (SID) of the next item from a user's interaction history. Existing training objectives mainly supervise target SID tokens, while on-policy distillation (OPD) provides denser supervision through a teacher's probability distribution over all tokens for more effective and efficient training. However, OPD can aggravate popularity bias: teacher feedback favors popular-item tokens, transferring this preference to the student, while student rollout favors popular items, leaving unpopular-item SID paths rarely generated and supervised. These two sources of bias reinforce each other and reduce recommendation diversity. We propose HERO, a diversity-aware OPD framework. Heterogeneous teacher distillation trains two teachers specializing in popular and unpopular items and routes each training sample to the teacher matching the popularity group of its ground-truth item. The student learns from the routed teacher's probability distribution along its own generated SIDs. Diversity-aware item rollout incorporates ground-truth SIDs of unpopular items into training, covering SID paths that the student rarely generates. Only the student is used for inference. Offline experiments on an industrial dataset and three public datasets further demonstrate its effectiveness, diversity, and robustness. Moreover, we deploy HERO in the "Guess What You Like" module on the 1688 homepage, where a 15-day online A/B test shows a 3.412% increase in gross merchandise value (GMV).