# Collaborative Memory Network for Recommendation Systems

arXiv: https://arxiv.org/abs/1804.10862 · HTML: https://mlsanity.com/paper/1804.10862/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2018.

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

- [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021)
- [SimpleX: A Simple and Strong Baseline for Collaborative Filtering](https://mlsanity.com/paper/2109.12613/) (2021)
- [How Powerful is Graph Convolution for Recommendation?](https://mlsanity.com/paper/2108.07567/) (2021)
- [Blurring-Sharpening Process Models for Collaborative Filtering](https://mlsanity.com/paper/2211.09324/) (2022)
- [LT-OCF: Learnable-Time ODE-based Collaborative Filtering](https://mlsanity.com/paper/2108.06208/) (2021)
- [Linear, or Non-Linear, That is the Question!](https://mlsanity.com/paper/2111.07265/) (2021)
- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [Collaboration-Aware Graph Convolutional Network for Recommender Systems](https://mlsanity.com/paper/2207.06221/) (2022)
- [Robust Collaborative Filtering to Popularity Distribution Shift](https://mlsanity.com/paper/2310.10696/) (2023)
- [On Estimating Recommendation Evaluation Metrics under Sampling](https://mlsanity.com/paper/2103.01474/) (2021)
- [SiReN: Sign-Aware Recommendation Using Graph Neural Networks](https://mlsanity.com/paper/2108.08735/) (2021)
- [On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering](https://mlsanity.com/paper/2306.03624/) (2023)
- [Supervised Contrastive Learning for Recommendation](https://mlsanity.com/paper/2201.03144/) (2022)
- [TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering](https://mlsanity.com/paper/2211.04266/) (2022)
- [User Consented Federated Recommender System Against Personalized Attribute Inference Attack](https://mlsanity.com/paper/2312.16203/) (2023)
- [Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order](https://mlsanity.com/paper/2301.00867/) (2023)
- [Graph Neural Ordinary Differential Equations-based method for Collaborative Filtering](https://mlsanity.com/paper/2311.12329/) (2023)
- [Explore User Neighborhood for Real-time E-commerce Recommendation](https://mlsanity.com/paper/2103.00442/) (2021)
- [Deep Learning-based Online Alternative Product Recommendations at Scale](https://mlsanity.com/paper/2104.07572/) (2021)
- [Learning from a Learning User for Optimal Recommendations](https://mlsanity.com/paper/2202.01879/) (2022)
- [Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis](https://mlsanity.com/paper/2308.00404/) (2023)
- [Neuro-Symbolic Recommendation Model based on Logic Query](https://mlsanity.com/paper/2309.07594/) (2023)
- [Attentive Graph-based Text-aware Preference Modeling for Top-N Recommendation](https://mlsanity.com/paper/2305.12976/) (2023)
- [Quaternion-Based Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2111.10536/) (2021)
- [Robust Recommendation with Implicit Feedback via Eliminating the Effects of Unexpected Behaviors](https://mlsanity.com/paper/2112.11023/) (2021)
- [SPR:Supervised Personalized Ranking Based on Prior Knowledge for Recommendation](https://mlsanity.com/paper/2207.03197/) (2022)
- [A Social-aware Gaussian Pre-trained Model for Effective Cold-start Recommendation](https://mlsanity.com/paper/2311.15790/) (2023)
- [Semantic-Enhanced Relational Metric Learning for Recommender Systems](https://mlsanity.com/paper/2406.10246/) (2024)
- [Collaborative Filtering Through Weighted Similarities of User and Item Embeddings](https://mlsanity.com/paper/2604.15573/) (2026)
- [MEMOIR: Temporal Behavioral Memory for Recommendation Across the Preference-Drift Spectrum](https://mlsanity.com/paper/2607.23986/) (2026)

## Abstract (arXiv)

Recommendation systems play a vital role to keep users engaged with personalized content in modern online platforms. Deep learning has revolutionized many research fields and there is a recent surge of interest in applying it to collaborative filtering (CF). However, existing methods compose deep learning architectures with the latent factor model ignoring a major class of CF models, neighborhood or memory-based approaches. We propose Collaborative Memory Networks (CMN), a deep architecture to unify the two classes of CF models capitalizing on the strengths of the global structure of latent factor model and local neighborhood-based structure in a nonlinear fashion. Motivated by the success of Memory Networks, we fuse a memory component and neural attention mechanism as the neighborhood component. The associative addressing scheme with the user and item memories in the memory module encodes complex user-item relations coupled with the neural attention mechanism to learn a user-item specific neighborhood. Finally, the output module jointly exploits the neighborhood with the user and item memories to produce the ranking score. Stacking multiple memory modules together yield deeper architectures capturing increasingly complex user-item relations. Furthermore, we show strong connections between CMN components, memory networks and the three classes of CF models. Comprehensive experimental results demonstrate the effectiveness of CMN on three public datasets outperforming competitive baselines. Qualitative visualization of the attention weights provide insight into the model's recommendation process and suggest the presence of higher order interactions.