# CosRec: 2D Convolutional Neural Networks for Sequential Recommendation

arXiv: https://arxiv.org/abs/1908.09972 · HTML: https://mlsanity.com/paper/1908.09972/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2019.

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

- [Intent Contrastive Learning for Sequential Recommendation](https://mlsanity.com/paper/2202.02519/) (2022)
- [Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation](https://mlsanity.com/paper/2503.22675/) (2025)
- [Sparse-Interest Network for Sequential Recommendation](https://mlsanity.com/paper/2102.09267/) (2021)
- [Sequential Recommendation via Stochastic Self-Attention](https://mlsanity.com/paper/2201.06035/) (2022)
- [Decoupled Side Information Fusion for Sequential Recommendation](https://mlsanity.com/paper/2204.11046/) (2022)
- [Personalized Showcases: Generating Multi-Modal Explanations for Recommendations](https://mlsanity.com/paper/2207.00422/) (2022)
- [CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation](https://mlsanity.com/paper/2405.02429/) (2024)
- [Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation](https://mlsanity.com/paper/2106.06165/) (2021)
- [Graph Masked Autoencoder for Sequential Recommendation](https://mlsanity.com/paper/2305.04619/) (2023)
- [Dynamic Memory based Attention Network for Sequential Recommendation](https://mlsanity.com/paper/2102.09269/) (2021)
- [TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation](https://mlsanity.com/paper/2502.02854/) (2025)
- [Text Matching Improves Sequential Recommendation by Reducing Popularity Biases](https://mlsanity.com/paper/2308.14029/) (2023)
- [APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation](https://mlsanity.com/paper/2311.02816/) (2023)
- [End-to-end Learnable Clustering for Intent Learning in Recommendation](https://mlsanity.com/paper/2401.05975/) (2024)
- [Data Augmentation Strategies for Improving Sequential Recommender Systems](https://mlsanity.com/paper/2203.14037/) (2022)
- [Modeling User Viewing Flow Using Large Language Models for Article Recommendation](https://mlsanity.com/paper/2311.07619/) (2023)
- [IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation](https://mlsanity.com/paper/2208.04600/) (2022)
- [Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational Transformer](https://mlsanity.com/paper/2210.13572/) (2022)
- [Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation](https://mlsanity.com/paper/2301.12197/) (2023)
- [Symmetry Structured Convolutional Neural Networks](https://mlsanity.com/paper/2203.02056/) (2022)
- [Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training](https://mlsanity.com/paper/2304.05492/) (2023)
- [Denoising Time Cycle Modeling for Recommendation](https://mlsanity.com/paper/2402.02718/) (2024)
- [Knowing Isn't Understanding: Re-grounding Generative Proactivity with Epistemic and Behavioral Insight](https://mlsanity.com/paper/2602.15259/) (2026)
- [Sequential Recommendation with Controllable Diversification: Representation Degeneration and Diversity](https://mlsanity.com/paper/2306.11986/) (2023)
- [Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2405.12473/) (2024)
- [ELASTIC: Efficient Linear Attention for Sequential Interest Compression](https://mlsanity.com/paper/2408.09380/) (2024)
- [Recommender Transformers with Behavior Pathways](https://mlsanity.com/paper/2206.06804/) (2022)
- [COUPA: An Industrial Recommender System for Online to Offline Service Platforms](https://mlsanity.com/paper/2304.12549/) (2023)
- [Compositions of Variant Experts for Integrating Short-Term and Long-Term Preferences](https://mlsanity.com/paper/2506.23170/) (2025)
- [Enhancing Temporal Sensitivity of Large Language Model for Recommendation with Counterfactual Tuning](https://mlsanity.com/paper/2507.03047/) (2025)

## Abstract (arXiv)

Sequential patterns play an important role in building modern recommender systems. To this end, several recommender systems have been built on top of Markov Chains and Recurrent Models (among others). Although these sequential models have proven successful at a range of tasks, they still struggle to uncover complex relationships nested in user purchase histories. In this paper, we argue that modeling pairwise relationships directly leads to an efficient representation of sequential features and captures complex item correlations. Specifically, we propose a 2D convolutional network for sequential recommendation (CosRec). It encodes a sequence of items into a three-way tensor; learns local features using 2D convolutional filters; and aggregates high-order interactions in a feedforward manner. Quantitative results on two public datasets show that our method outperforms both conventional methods and recent sequence-based approaches, achieving state-of-the-art performance on various evaluation metrics.