Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering

By Reinhard Heckel, Michail Vlachos, Thomas Parnell, Celestine Mendler-Dünner. 2016

59 citations recorded by the source corpus.

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).

Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Management Science and Operations Research

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Citations in the corpus

Designing Explanation Interfaces for Transparency and Beyond (2019, methodology): “[4] that was used to present overlapping user-item co-clusters in a scalable and interpretable product recommendation model.”
Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis (2018, unknown): “[18] proposed to identify overlapping co-clusters of users and items with similar patterns, and confine the latent factors to represent users’ and items’ participation in the clusters.”
Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems (2018, methodology): “Heckel et al. [18] use a generative model to identify overlapping groups of users and items with similar patterns which they use to constrain the latent factors to explicitly model user and item participation.”