Interest modeling in recommender system has been a constant topic for
improving user experience, and typical interest modeling tasks (e.g.
multi-interest, long-tail interest and long-term interest) have been
investigated in many existing works. However, most of them only consider one
interest in isolation, while neglecting their interrelationships. In this
paper, we argue that these tasks suffer from a common "interest amnesia"
problem, and a solution exists to mitigate it simultaneously. We figure that
long-term cues can be the cornerstone since they reveal multi-interest and
clarify long-tail interest. Inspired by the observation, we propose a novel and
unified framework in the retrieval stage, "Trinity", to solve interest amnesia
problem and improve multiple interest modeling tasks. We construct a real-time
clustering system that enables us to project items into enumerable clusters,
and calculate statistical interest histograms over these clusters. Based on
these histograms, Trinity recognizes underdelivered themes and remains stable
when facing emerging hot topics. Trinity is more appropriate for large-scale
industry scenarios because of its modest computational overheads. Its derived
retrievers have been deployed on the recommender system of Douyin,
significantly improving user experience and retention. We believe that such
practical experience can be well generalized to other scenarios.

我们提出了一种名为 “Trinity” 的新型和统一的框架，在检索阶段解决了兴趣遗忘问题和改进了多兴趣建模任务。在 Douyin 的推荐系统中部署后，显著改善了用户体验和留存，我们相信这种实际经验可以推广到其他场景。