# Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network

arXiv: https://arxiv.org/abs/2110.04002 · HTML: https://mlsanity.com/paper/2110.04002/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 55 references that are in the map.
- First posted on arXiv 8 Oct 2021.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [Neural Factorization Machines for Sparse Predictive Analytics](https://mlsanity.com/paper/1708.05027/) (2017)
- [Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](https://mlsanity.com/paper/1904.08030/) (2019)
- [Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems](https://mlsanity.com/paper/1905.04413/) (2019)
- [Fast Matrix Factorization for Online Recommendation with Implicit Feedback](https://mlsanity.com/paper/1708.05024/) (2017)
- [Representation Learning for Attributed Multiplex Heterogeneous Network](https://mlsanity.com/paper/1905.01669/) (2019)
- [Joint Deep Modeling of Users and Items Using Reviews for Recommendation](https://mlsanity.com/paper/1701.04783/) (2017)
- [Session-based Social Recommendation via Dynamic Graph Attention Networks](https://mlsanity.com/paper/1902.09362/) (2019)
- [STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems](https://mlsanity.com/paper/1905.13129/) (2019)
- [Memory Augmented Graph Neural Networks for Sequential Recommendation](https://mlsanity.com/paper/1912.11730/) (2019)
- [Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking](https://mlsanity.com/paper/1707.05176/) (2017)
- [Multi-Graph Convolution Collaborative Filtering](https://mlsanity.com/paper/2001.00267/) (2020)
- [Multi-Pointer Co-Attention Networks for Recommendation](https://mlsanity.com/paper/1801.09251/) (2018)
- [A Neural Autoregressive Approach to Collaborative Filtering](https://mlsanity.com/paper/1605.09477/) (2016)
- [Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation](https://mlsanity.com/paper/1904.12796/) (2019)
- [Hybrid Recommender System based on Autoencoders](https://mlsanity.com/paper/1606.07659/) (2016)
- [Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling](https://mlsanity.com/paper/1911.03883/) (2019)
- [Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation](https://mlsanity.com/paper/1804.04327/) (2018)
- [Collaborative Filtering with User-Item Co-Autoregressive Models](https://mlsanity.com/paper/1612.07146/) (2016)
- [Compositional Coding for Collaborative Filtering](https://mlsanity.com/paper/1905.03752/) (2019)

## Built on by (most cited first, 28 of 28)

- [Contrastive Meta Learning with Behavior Multiplicity for Recommendation](https://mlsanity.com/paper/2202.08523/) (2022)
- [Multiplex Heterogeneous Graph Convolutional Network](https://mlsanity.com/paper/2208.06129/) (2022)
- [A Survey on User Behavior Modeling in Recommender Systems](https://mlsanity.com/paper/2302.11087/) (2023)
- [Multi-Behavior Sequential Recommendation with Temporal Graph Transformer](https://mlsanity.com/paper/2206.02687/) (2022)
- [ContextGNN: Beyond Two-Tower Recommendation Systems](https://mlsanity.com/paper/2411.19513/) (2024)
- [Knowledge Enhancement for Contrastive Multi-Behavior Recommendation](https://mlsanity.com/paper/2301.05403/) (2023)
- [Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendation](https://mlsanity.com/paper/2402.14473/) (2024)
- [Multi-behavior Self-supervised Learning for Recommendation](https://mlsanity.com/paper/2305.18238/) (2023)
- [Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling](https://mlsanity.com/paper/2201.02307/) (2022)
- [Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation](https://mlsanity.com/paper/2208.01849/) (2022)
- [Instant Representation Learning for Recommendation over Large Dynamic Graphs](https://mlsanity.com/paper/2305.18622/) (2023)
- [DiffGraph: Heterogeneous Graph Diffusion Model](https://mlsanity.com/paper/2501.02313/) (2025)
- [Tricolore: Multi-Behavior User Profiling for Enhanced Candidate Generation in Recommender Systems](https://mlsanity.com/paper/2505.02120/) (2025)
- [Dual-Channel Multiplex Graph Neural Networks for Recommendation](https://mlsanity.com/paper/2403.11624/) (2024)
- [Compressed Interaction Graph based Framework for Multi-behavior Recommendation](https://mlsanity.com/paper/2303.02418/) (2023)
- [RecKG: Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/2501.03598/) (2025)
- [Multi-Behavior Recommender Systems: A Survey](https://mlsanity.com/paper/2503.06963/) (2025)
- [Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation](https://mlsanity.com/paper/2408.11372/) (2024)
- [Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems](https://mlsanity.com/paper/2201.03158/) (2022)
- [HMAR: Hierarchical Masked Attention for Multi-Behaviour Recommendation](https://mlsanity.com/paper/2405.09638/) (2024)
- [Multi-Channel Hypergraph Contrastive Learning for Matrix Completion](https://mlsanity.com/paper/2411.01376/) (2024)
- [MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation](https://mlsanity.com/paper/2603.25126/) (2026)
- [A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendation](https://mlsanity.com/paper/2508.19507/) (2025)
- [Rethinking Purity and Diversity in Multi-Behavior Sequential Recommendation from the Frequency Perspective](https://mlsanity.com/paper/2508.20427/) (2025)
- [Cross-Scenario Unified Modeling of User Interests at Billion Scale](https://mlsanity.com/paper/2510.14788/) (2025)
- [From Agnostic to Specific: Latent Preference Diffusion for Multi-Behavior Sequential Recommendation](https://mlsanity.com/paper/2602.23132/) (2026)
- [RaDAR: Relation-aware Diffusion-Asymmetric Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2603.16800/) (2026)
- [Beyond Observed Auxiliary Relations: Environment-Conditioned Modeling for Multi-Behavior Recommendation](https://mlsanity.com/paper/2608.22920/) (2026)

## Abstract (arXiv)

Capturing users' precise preferences is of great importance in various recommender systems (eg., e-commerce platforms), which is the basis of how to present personalized interesting product lists to individual users. In spite of significant progress has been made to consider relations between users and items, most of the existing recommendation techniques solely focus on singular type of user-item interactions. However, user-item interactive behavior is often exhibited with multi-type (e.g., page view, add-to-favorite and purchase) and inter-dependent in nature. The overlook of multiplex behavior relations can hardly recognize the multi-modal contextual signals across different types of interactions, which limit the feasibility of current recommendation methods. To tackle the above challenge, this work proposes a Memory-Augmented Transformer Networks (MATN), to enable the recommendation with multiplex behavioral relational information, and joint modeling of type-specific behavioral context and type-wise behavior inter-dependencies, in a fully automatic manner. In our MATN framework, we first develop a transformer-based multi-behavior relation encoder, to make the learned interaction representations be reflective of the cross-type behavior relations. Furthermore, a memory attention network is proposed to supercharge MATN capturing the contextual signals of different types of behavior into the category-specific latent embedding space. Finally, a cross-behavior aggregation component is introduced to promote the comprehensive collaboration across type-aware interaction behavior representations, and discriminate their inherent contributions in assisting recommendations. Extensive experiments on two benchmark datasets and a real-world e-commence user behavior data demonstrate significant improvements obtained by MATN over baselines. Codes are available at: https://github.com/akaxlh/MATN.