# Joint Optimization of Tree-based Index and Deep Model for Recommender Systems

arXiv: https://arxiv.org/abs/1902.07565 · HTML: https://mlsanity.com/paper/1902.07565/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2019.

## Built on by (most cited first, 30 of 37)

- [End-to-End User Behavior Retrieval in Click-Through RatePrediction Model](https://mlsanity.com/paper/2108.04468/) (2021)
- [Constructing Tree-based Index for Efficient and Effective Dense Retrieval](https://mlsanity.com/paper/2304.11943/) (2023)
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- [Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation](https://mlsanity.com/paper/2202.10226/) (2022)
- [Revisiting Neural Retrieval on Accelerators](https://mlsanity.com/paper/2306.04039/) (2023)
- [MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration](https://mlsanity.com/paper/2202.04348/) (2022)
- [Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems](https://mlsanity.com/paper/2206.00407/) (2022)
- [Long-Sequence Recommendation Models Need Decoupled Embeddings](https://mlsanity.com/paper/2410.02604/) (2024)
- [Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential Recommendations](https://mlsanity.com/paper/2401.06633/) (2024)
- [One Backward from Ten Forward, Subsampling for Large-Scale Deep Learning](https://mlsanity.com/paper/2104.13114/) (2021)
- [Large Language Model as Universal Retriever in Industrial-Scale Recommender System](https://mlsanity.com/paper/2502.03041/) (2025)
- [Truncation-Free Matching System for Display Advertising at Alibaba](https://mlsanity.com/paper/2102.09283/) (2021)
- [HybridGNN: Learning Hybrid Representation in Multiplex Heterogeneous Networks](https://mlsanity.com/paper/2208.02068/) (2022)
- [Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/2209.12212/) (2022)
- [Givens Coordinate Descent Methods for Rotation Matrix Learning in Trainable Embedding Indexes](https://mlsanity.com/paper/2203.05082/) (2022)
- [Practice with Graph-based ANN Algorithms on Sparse Data: Chi-square Two-tower model, HNSW, Sign Cauchy Projections](https://mlsanity.com/paper/2306.07607/) (2023)
- [Context-aware Tree-based Deep Model for Recommender Systems](https://mlsanity.com/paper/2109.10602/) (2021)
- [A General Framework for Debiasing in CTR Prediction](https://mlsanity.com/paper/2112.02767/) (2021)
- [Tree-based Text-Vision BERT for Video Search in Baidu Video Advertising](https://mlsanity.com/paper/2209.08759/) (2022)
- [CoopHash: Cooperative Learning of Multipurpose Descriptor and Contrastive Pair Generator via Variational MCMC Teaching for Supervised Image Hashing](https://mlsanity.com/paper/2210.04288/) (2022)
- [DURENDAL: Graph deep learning framework for temporal heterogeneous networks](https://mlsanity.com/paper/2310.00336/) (2023)
- [pEBR: A Probabilistic Approach to Embedding Based Retrieval](https://mlsanity.com/paper/2410.19349/) (2024)
- [CRM: Retrieval Model with Controllable Condition](https://mlsanity.com/paper/2412.13844/) (2024)
- [MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendation](https://mlsanity.com/paper/2508.14515/) (2025)
- [Incentive Compatible Pareto Alignment for Multi-Source Large Graphs](https://mlsanity.com/paper/2112.02792/) (2021)
- [A Unified Framework for Campaign Performance Forecasting in Online Display Advertising](https://mlsanity.com/paper/2202.11877/) (2022)
- [WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models](https://mlsanity.com/paper/2202.13616/) (2022)
- [Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction](https://mlsanity.com/paper/2311.10764/) (2023)
- [Multi-Tower Multi-Interest Recommendation with User Representation Repel](https://mlsanity.com/paper/2403.05122/) (2024)
- [Deep Uncertainty-Based Explore for Index Construction and Retrieval in Recommendation System](https://mlsanity.com/paper/2408.00799/) (2024)

## Abstract (arXiv)

Large-scale industrial recommender systems are usually confronted with computational problems due to the enormous corpus size. To retrieve and recommend the most relevant items to users under response time limits, resorting to an efficient index structure is an effective and practical solution. The previous work Tree-based Deep Model (TDM) citezhu2018learning greatly improves recommendation accuracy using tree index. By indexing items in a tree hierarchy and training a user-node preference prediction model satisfying a max-heap like property in the tree, TDM provides logarithmic computational complexity w.r.t. the corpus size, enabling the use of arbitrary advanced models in candidate retrieval and recommendation. In tree-based recommendation methods, the quality of both the tree index and the user-node preference prediction model determines the recommendation accuracy for the most part. We argue that the learning of tree index and preference model has interdependence. Our purpose, in this paper, is to develop a method to jointly learn the index structure and user preference prediction model. In our proposed joint optimization framework, the learning of index and user preference prediction model are carried out under a unified performance measure. Besides, we come up with a novel hierarchical user preference representation utilizing the tree index hierarchy. Experimental evaluations with two large-scale real-world datasets show that the proposed method improves recommendation accuracy significantly. Online A/B test results at a display advertising platform also demonstrate the effectiveness of the proposed method in production environments.