# ImplicitSLIM and How it Improves Embedding-based Collaborative Filtering

arXiv: https://arxiv.org/abs/2406.00198 · HTML: https://mlsanity.com/paper/2406.00198/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 48 references that are in the map.
- First posted on arXiv 31 May 2024.

## Builds on (most cited first, 17 of 17)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [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)
- [Fast Matrix Factorization for Online Recommendation with Implicit Feedback](https://mlsanity.com/paper/1708.05024/) (2017)
- [Embarrassingly Shallow Autoencoders for Sparse Data](https://mlsanity.com/paper/1905.03375/) (2019)
- [UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation](https://mlsanity.com/paper/2110.15114/) (2021)
- [RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback](https://mlsanity.com/paper/1912.11160/) (2019)
- [BARS: Towards Open Benchmarking for Recommender Systems](https://mlsanity.com/paper/2205.09626/) (2022)
- [How Powerful is Graph Convolution for Recommendation?](https://mlsanity.com/paper/2108.07567/) (2021)
- [A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation](https://mlsanity.com/paper/2207.07483/) (2022)
- [Towards a Better Understanding of Linear Models for Recommendation](https://mlsanity.com/paper/2105.12937/) (2021)
- [Locally Linear Embedding and its Variants: Tutorial and Survey](https://mlsanity.com/paper/2011.10925/) (2020)
- [Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks](https://mlsanity.com/paper/2008.06716/) (2020)
- [Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms](https://mlsanity.com/paper/1911.00936/) (2019)
- [On the Definiteness of the Weighted Laplacian and its Connection to Effective Resistance](https://mlsanity.com/paper/1408.2187/) (2014)
- [Collaborative Item Embedding Model for Implicit Feedback Data](https://mlsanity.com/paper/1805.05005/) (2018)

## Abstract (arXiv)

We present ImplicitSLIM, a novel unsupervised learning approach for sparse high-dimensional data, with applications to collaborative filtering. Sparse linear methods (SLIM) and their variations show outstanding performance, but they are memory-intensive and hard to scale. ImplicitSLIM improves embedding-based models by extracting embeddings from SLIM-like models in a computationally cheap and memory-efficient way, without explicit learning of heavy SLIM-like models. We show that ImplicitSLIM improves performance and speeds up convergence for both state of the art and classical collaborative filtering methods. The source code for ImplicitSLIM, related models, and applications is available at https://github.com/ilya-shenbin/ImplicitSLIM.