Scalable and interpretable product recommendations via overlapping co-clustering

Reinhard Heckel, Michail Vlachos, Thomas Parnell, Celestine Duenner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

43 Scopus citations

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).

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
PublisherIEEE Computer Society
Pages1033-1044
Number of pages12
ISBN (Electronic)9781509065431
DOIs
StatePublished - 16 May 2017
Externally publishedYes
Event33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States
Duration: 19 Apr 201722 Apr 2017

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Conference

Conference33rd IEEE International Conference on Data Engineering, ICDE 2017
Country/TerritoryUnited States
CitySan Diego
Period19/04/1722/04/17

Fingerprint

Dive into the research topics of 'Scalable and interpretable product recommendations via overlapping co-clustering'. Together they form a unique fingerprint.

Cite this