# Scalable and interpretable product recommendations via overlapping co-clustering

arXiv: https://arxiv.org/abs/1604.02071 · HTML: https://mlsanity.com/paper/1604.02071/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2016.

## Built on by (most cited first, 4 of 4)

- [Efficient High-Quality Clustering for Large Bipartite Graphs](https://mlsanity.com/paper/2312.16926/) (2023)
- [LANE: Logic Alignment of Non-tuning Large Language Models and Online Recommendation Systems for Explainable Reason Generation](https://mlsanity.com/paper/2407.02833/) (2024)
- [Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation](https://mlsanity.com/paper/2207.05959/) (2022)
- [Counterfactually Evaluating Explanations in Recommender Systems](https://mlsanity.com/paper/2203.01310/) (2022)

## Abstract (arXiv)

We consider the problem of generating interpretable recommendations by identifying overlapping co-clusters of clients and products, based only on positive or implicit feedback. Our approach is applicable on very large datasets because it exhibits almost linear complexity in the input examples and the number of co-clusters. We show, both on real industrial data and on publicly available datasets, that the recommendation accuracy of our algorithm is competitive to that of state-of-art matrix factorization techniques. In addition, our technique has the advantage of offering recommendations that are textually and visually interpretable. Finally, we examine how to implement our technique efficiently on Graphical Processing Units (GPUs).