  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.
