Scalable and interpretable product recommendations via overlapping co-clustering
This provides scalable and interpretable recommendations for e-commerce or similar platforms, though it is incremental as it builds on existing co-clustering methods.
The paper tackles the problem of generating interpretable product recommendations by identifying overlapping co-clusters of clients and products from positive or implicit feedback, achieving competitive accuracy to state-of-the-art matrix factorization techniques on real and public datasets.
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).