Scalable and interpretable product recommendations via overlapping co-clustering

TL;DR

This paper introduces a scalable, interpretable co-clustering method for product recommendations that performs well on large datasets and offers clear explanations, with efficient GPU implementation.

Contribution

The paper presents a novel overlapping co-clustering algorithm for recommendations that is scalable, interpretable, and competitive with state-of-the-art matrix factorization methods.

Findings

Competitive recommendation accuracy on real and public datasets.

Provides interpretable, textually and visually understandable recommendations.

Efficient GPU implementation for large-scale data processing.

Abstract

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).