# A Generic Coordinate Descent Framework for Learning from Implicit Feedback

arXiv: https://arxiv.org/abs/1611.04666 · HTML: https://mlsanity.com/paper/1611.04666/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2016.

## Built on by (most cited first, 16 of 16)

- [Revisiting the Performance of iALS on Item Recommendation Benchmarks](https://mlsanity.com/paper/2110.14037/) (2021)
- [Item Recommendation from Implicit Feedback](https://mlsanity.com/paper/2101.08769/) (2021)
- [On the Theories Behind Hard Negative Sampling for Recommendation](https://mlsanity.com/paper/2302.03472/) (2023)
- [Graph Collaborative Signals Denoising and Augmentation for Recommendation](https://mlsanity.com/paper/2304.03344/) (2023)
- [SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation](https://mlsanity.com/paper/2405.00287/) (2024)
- [iALS++: Speeding up Matrix Factorization with Subspace Optimization](https://mlsanity.com/paper/2110.14044/) (2021)
- [Learning Complex Users' Preferences for Recommender Systems](https://mlsanity.com/paper/2107.01529/) (2021)
- [Safe Collaborative Filtering](https://mlsanity.com/paper/2306.05292/) (2023)
- [IDVT: Interest-aware Denoising and View-guided Tuning for Social Recommendation](https://mlsanity.com/paper/2308.15926/) (2023)
- [Represent Items by Items: An Enhanced Representation of the Target Item for Recommendation](https://mlsanity.com/paper/2104.12483/) (2021)
- [Consistent Collaborative Filtering via Tensor Decomposition](https://mlsanity.com/paper/2201.11936/) (2022)
- [SR-GCL: Session-Based Recommendation with Global Context Enhanced Augmentation in Contrastive Learning](https://mlsanity.com/paper/2209.10807/) (2022)
- [HEAT: A Highly Efficient and Affordable Training System for Collaborative Filtering Based Recommendation on CPUs](https://mlsanity.com/paper/2304.07334/) (2023)
- [The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning](https://mlsanity.com/paper/2312.03120/) (2023)
- [Dual-Channel Latent Factor Analysis Enhanced Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2408.04838/) (2024)
- [NDCG-Consistent Softmax Approximation with Accelerated Convergence](https://mlsanity.com/paper/2506.09454/) (2025)

## Abstract (arXiv)

In recent years, interest in recommender research has shifted from explicit feedback towards implicit feedback data. A diversity of complex models has been proposed for a wide variety of applications. Despite this, learning from implicit feedback is still computationally challenging. So far, most work relies on stochastic gradient descent (SGD) solvers which are easy to derive, but in practice challenging to apply, especially for tasks with many items. For the simple matrix factorization model, an efficient coordinate descent (CD) solver has been previously proposed. However, efficient CD approaches have not been derived for more complex models. In this paper, we provide a new framework for deriving efficient CD algorithms for complex recommender models. We identify and introduce the property of k-separable models. We show that k-separability is a sufficient property to allow efficient optimization of implicit recommender problems with CD. We illustrate this framework on a variety of state-of-the-art models including factorization machines and Tucker decomposition. To summarize, our work provides the theory and building blocks to derive efficient implicit CD algorithms for complex recommender models.