# SimpleX: A Simple and Strong Baseline for Collaborative Filtering

arXiv: https://arxiv.org/abs/2109.12613 · HTML: https://mlsanity.com/paper/2109.12613/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 42 references that are in the map.
- First posted on arXiv 26 Sep 2021.

## Builds on (most cited first, 21 of 21)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (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)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Self-supervised Graph Learning for Recommendation](https://mlsanity.com/paper/2010.10783/) (2020)
- [Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach](https://mlsanity.com/paper/2001.10167/) (2020)
- [Disentangled Graph Collaborative Filtering](https://mlsanity.com/paper/2007.01764/) (2020)
- [Learning Disentangled Representations for Recommendation](https://mlsanity.com/paper/1910.14238/) (2019)
- [Embarrassingly Shallow Autoencoders for Sparse Data](https://mlsanity.com/paper/1905.03375/) (2019)
- [NPA: Neural News Recommendation with Personalized Attention](https://mlsanity.com/paper/1907.05559/) (2019)
- [RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback](https://mlsanity.com/paper/1912.11160/) (2019)
- [Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters](https://mlsanity.com/paper/2006.15516/) (2020)
- [Collaborative Memory Network for Recommendation Systems](https://mlsanity.com/paper/1804.10862/) (2018)
- [Sampler Design for Implicit Feedback Data by Noisy-label Robust Learning](https://mlsanity.com/paper/2007.07204/) (2020)
- [Adaptive Semantic-Visual Tree for Hierarchical Embeddings](https://mlsanity.com/paper/2003.03707/) (2020)
- [NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation](https://mlsanity.com/paper/2010.12256/) (2020)

## Built on by (most cited first, 30 of 65)

- [Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)](https://mlsanity.com/paper/2203.13366/) (2022)
- [UP5: Unbiased Foundation Model for Fairness-aware Recommendation](https://mlsanity.com/paper/2305.12090/) (2023)
- [Blurring-Sharpening Process Models for Collaborative Filtering](https://mlsanity.com/paper/2211.09324/) (2022)
- [MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation](https://mlsanity.com/paper/2308.11175/) (2023)
- [Linear-Time Graph Neural Networks for Scalable Recommendations](https://mlsanity.com/paper/2402.13973/) (2024)
- [LLMRec: Benchmarking Large Language Models on Recommendation Task](https://mlsanity.com/paper/2308.12241/) (2023)
- [Towards Graph Contrastive Learning: A Survey and Beyond](https://mlsanity.com/paper/2405.11868/) (2024)
- [HICF: Hyperbolic Informative Collaborative Filtering](https://mlsanity.com/paper/2207.09051/) (2022)
- [Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendation](https://mlsanity.com/paper/2302.04775/) (2023)
- [Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation](https://mlsanity.com/paper/2311.18213/) (2023)
- [Large Language Models are Not Stable Recommender Systems](https://mlsanity.com/paper/2312.15746/) (2023)
- [Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering](https://mlsanity.com/paper/2402.11523/) (2024)
- [Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation](https://mlsanity.com/paper/2403.00844/) (2024)
- [Toward a Better Understanding of Loss Functions for Collaborative Filtering](https://mlsanity.com/paper/2308.06091/) (2023)
- [LD4MRec: Simplifying and Powering Diffusion Model for Multimedia Recommendation](https://mlsanity.com/paper/2309.15363/) (2023)
- [LightLM: A Lightweight Deep and Narrow Language Model for Generative Recommendation](https://mlsanity.com/paper/2310.17488/) (2023)
- [Enhancing High-order Interaction Awareness in LLM-based Recommender Model](https://mlsanity.com/paper/2409.19979/) (2024)
- [Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty](https://mlsanity.com/paper/2404.08301/) (2024)
- [Graph Spectral Filtering with Chebyshev Interpolation for Recommendation](https://mlsanity.com/paper/2505.00552/) (2025)
- [Learning Explicit User Interest Boundary for Recommendation](https://mlsanity.com/paper/2111.11026/) (2021)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering](https://mlsanity.com/paper/2305.12768/) (2023)
- [Revisiting Recommendation Loss Functions through Contrastive Learning (Technical Report)](https://mlsanity.com/paper/2312.08520/) (2023)
- [BSL: Understanding and Improving Softmax Loss for Recommendation](https://mlsanity.com/paper/2312.12882/) (2023)
- [Test Time Embedding Normalization for Popularity Bias Mitigation](https://mlsanity.com/paper/2308.11288/) (2023)
- [SimCE: Simplifying Cross-Entropy Loss for Collaborative Filtering](https://mlsanity.com/paper/2406.16170/) (2024)
- [Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation](https://mlsanity.com/paper/2508.18142/) (2025)
- [Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation](https://mlsanity.com/paper/2301.12197/) (2023)
- [Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis](https://mlsanity.com/paper/2308.00404/) (2023)
- [X2-Softmax: Margin Adaptive Loss Function for Face Recognition](https://mlsanity.com/paper/2312.05281/) (2023)

## Abstract (arXiv)

Collaborative filtering (CF) is a widely studied research topic in recommender systems. The learning of a CF model generally depends on three major components, namely interaction encoder, loss function, and negative sampling. While many existing studies focus on the design of more powerful interaction encoders, the impacts of loss functions and negative sampling ratios have not yet been well explored. In this work, we show that the choice of loss function as well as negative sampling ratio is equivalently important. More specifically, we propose the cosine contrastive loss (CCL) and further incorporate it to a simple unified CF model, dubbed SimpleX. Extensive experiments have been conducted on 11 benchmark datasets and compared with 29 existing CF models in total. Surprisingly, the results show that, under our CCL loss and a large negative sampling ratio, SimpleX can surpass most sophisticated state-of-the-art models by a large margin (e.g., max 48.5% improvement in NDCG@20 over LightGCN). We believe that SimpleX could not only serve as a simple strong baseline to foster future research on CF, but also shed light on the potential research direction towards improving loss function and negative sampling. Our source code will be available at https://reczoo.github.io/SimpleX.