Recommendations Made Easy

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