Recommendations Made Easy

Date

Authors

Guinness, Darren, 1990-
Karbasi, Seyedeh Paniz, 1986-
Nazarov, Rovshen
Speegle, Gregory David.

Access rights

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Fueled by ever-growing data, the need to provide recommendations for consumers, and the considerable domain knowledge required to implement distributed large scale graph solutions we sought to provide recommendations for users with minimal required knowledge. For this reason in this paper we implement a generalizable 'API-like' access to collaborative filtering. Three algorithms are introduced with three execution plans in order to accomplish the collaborative filtering functionality. Execution is based on memory constraints for scalability and our initial tests show promising results. We believe this method of large-scale generalized 'API-like' graph computation provides not only good trade-off between performance and required knowledge, but also the future of distributed graph computation.

Description

Keywords

Hadoop, MapReduce, Graphlab, Parallel, Hadoop MapReduce, Parallel Processing, Collaborative Filtering, Distributed Computation, API, Distributed Databases, Distributed Applications

Citation