# SDM: Sequential Deep Matching Model for Online Large-scale Recommender System

arXiv: https://arxiv.org/abs/1909.00385 · HTML: https://mlsanity.com/paper/1909.00385/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2019.

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

- [Recommendation Unlearning](https://mlsanity.com/paper/2201.06820/) (2022)
- [Sparse-Interest Network for Sequential Recommendation](https://mlsanity.com/paper/2102.09267/) (2021)
- [Lightweight Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/2108.11333/) (2021)
- [Embedding in Recommender Systems: A Survey](https://mlsanity.com/paper/2310.18608/) (2023)
- [Dynamic Memory based Attention Network for Sequential Recommendation](https://mlsanity.com/paper/2102.09269/) (2021)
- [Revisiting Neural Retrieval on Accelerators](https://mlsanity.com/paper/2306.04039/) (2023)
- [Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer](https://mlsanity.com/paper/2201.13311/) (2022)
- [One4all User Representation for Recommender Systems in E-commerce](https://mlsanity.com/paper/2106.00573/) (2021)
- [Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](https://mlsanity.com/paper/2203.15542/) (2022)
- [Denoising User-aware Memory Network for Recommendation](https://mlsanity.com/paper/2107.05474/) (2021)
- [Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation](https://mlsanity.com/paper/2208.11457/) (2022)
- [Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems](https://mlsanity.com/paper/2309.15646/) (2023)
- [Explore User Neighborhood for Real-time E-commerce Recommendation](https://mlsanity.com/paper/2103.00442/) (2021)
- [Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search](https://mlsanity.com/paper/2107.07173/) (2021)
- [IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation](https://mlsanity.com/paper/2208.04600/) (2022)
- [Triangle Graph Interest Network for Click-through Rate Prediction](https://mlsanity.com/paper/2202.02698/) (2022)
- [Reusable Self-Attention-based Recommender System for Fashion](https://mlsanity.com/paper/2211.16366/) (2022)
- [Exploiting Graph Structured Cross-Domain Representation for Multi-Domain Recommendation](https://mlsanity.com/paper/2302.05990/) (2023)
- [Denoising Long- and Short-term Interests for Sequential Recommendation](https://mlsanity.com/paper/2407.14743/) (2024)
- [Context-aware Tree-based Deep Model for Recommender Systems](https://mlsanity.com/paper/2109.10602/) (2021)
- [BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation](https://mlsanity.com/paper/2402.12733/) (2024)
- [Multiple Interest and Fine Granularity Network for User Modeling](https://mlsanity.com/paper/2112.02591/) (2021)
- [Hierarchical Multi-Interest Co-Network For Coarse-Grained Ranking](https://mlsanity.com/paper/2210.10547/) (2022)
- [Representation Learning of Tangled Key-Value Sequence Data for Early Classification](https://mlsanity.com/paper/2404.07454/) (2024)
- [Separated Contrastive Learning for Matching in Cross-domain Recommendation with Curriculum Scheduling](https://mlsanity.com/paper/2502.16239/) (2025)
- [MPFormer: Adaptive Framework for Industrial Multi-Task Personalized Sequential Retriever](https://mlsanity.com/paper/2508.20400/) (2025)
- [Time Matters: A Novel Real-Time Long- and Short-term User Interest Model for Click-Through Rate Prediction](https://mlsanity.com/paper/2511.06213/) (2025)
- [LLM-I2I: Boost Your Small Item2Item Recommendation Model with Large Language Model](https://mlsanity.com/paper/2512.21595/) (2025)
- [SLSREC: Self-Supervised Contrastive Learning for Adaptive Fusion of Long- and Short-Term User Interests](https://mlsanity.com/paper/2604.04530/) (2026)
- [Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/2607.19253/) (2026)

## Abstract (arXiv)

Capturing users' precise preferences is a fundamental problem in large-scale recommender system. Currently, item-based Collaborative Filtering (CF) methods are common matching approaches in industry. However, they are not effective to model dynamic and evolving preferences of users. In this paper, we propose a new sequential deep matching (SDM) model to capture users' dynamic preferences by combining short-term sessions and long-term behaviors. Compared with existing sequence-aware recommendation methods, we tackle the following two inherent problems in real-world applications: (1) there could exist multiple interest tendencies in one session. (2) long-term preferences may not be effectively fused with current session interests. Long-term behaviors are various and complex, hence those highly related to the short-term session should be kept for fusion. We propose to encode behavior sequences with two corresponding components: multi-head self-attention module to capture multiple types of interests and long-short term gated fusion module to incorporate long-term preferences. Successive items are recommended after matching between sequential user behavior vector and item embedding vectors. Offline experiments on real-world datasets show the superior performance of the proposed SDM. Moreover, SDM has been successfully deployed on online large-scale recommender system at Taobao and achieves improvements in terms of a range of commercial metrics.