# Trinity: Syncretizing Multi-/Long-tail/Long-term Interests All in One

arXiv: https://arxiv.org/abs/2402.02842 · HTML: https://mlsanity.com/paper/2402.02842/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction](https://mlsanity.com/paper/2006.05639/) (2020)
- [Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09248/) (2019)
- [Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](https://mlsanity.com/paper/1904.08030/) (2019)
- [Controllable Multi-Interest Framework for Recommendation](https://mlsanity.com/paper/2005.09347/) (2020)
- [Sparse-Interest Network for Sequential Recommendation](https://mlsanity.com/paper/2102.09267/) (2021)
- [Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval](https://mlsanity.com/paper/2110.05789/) (2021)
- [Dynamic Memory based Attention Network for Sequential Recommendation](https://mlsanity.com/paper/2102.09269/) (2021)
- [Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems](https://mlsanity.com/paper/2302.14532/) (2023)
- [Harnessing GANs for Zero-shot Learning of New Classes in Visual Speech Recognition](https://mlsanity.com/paper/1901.10139/) (2019)
- [Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling](https://mlsanity.com/paper/2106.07356/) (2021)

## Built on by (most cited first, 7 of 7)

- [HoME: Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou](https://mlsanity.com/paper/2408.05430/) (2024)
- [TransAct V2: Lifelong User Action Sequence Modeling on Pinterest Recommendation](https://mlsanity.com/paper/2506.02267/) (2025)
- [PERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching](https://mlsanity.com/paper/2506.18382/) (2025)
- [UniGRec: Unified Generative Recommendation with Soft Identifiers for End-to-End Optimization](https://mlsanity.com/paper/2601.17438/) (2026)
- [SPARC: Soft Probabilistic Adaptive multi-interest Retrieval Model via Codebooks for recommender system](https://mlsanity.com/paper/2508.09090/) (2025)
- [Rethinking ANN-based Retrieval: Multifaceted Learnable Index for Large-scale Recommendation System](https://mlsanity.com/paper/2602.16124/) (2026)
- [Real-Time Hard Negative Sampling via LLM-based Clustering for Large-Scale Two-Tower Retrieval](https://mlsanity.com/paper/2607.00448/) (2026)

## Abstract (arXiv)

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.