Uptrendz: API-Centric Real-time Recommendations in Multi-Domain Settings

TL;DR

Uptrendz is a flexible, API-centric platform enabling real-time, multi-domain recommendations, demonstrated through movie and startup recommendation use cases, facilitating easy adaptation, deployment, and evaluation.

Contribution

The paper introduces Uptrendz, a novel multi-domain recommendation platform that supports real-time, customizable recommendations via an API-centric approach, adaptable to diverse application domains.

Findings

Successfully set up a real-time movie recommender using MovieLens-100k.

Supported multiple domains simultaneously, including startup recommendations.

Demonstrated ease of adaptation, deployment, and evaluation of the system.

Abstract

In this work, we tackle the problem of adapting a real-time recommender system to multiple application domains, and their underlying data models and customization requirements. To do that, we present Uptrendz, a multi-domain recommendation platform that can be customized to provide real-time recommendations in an API-centric way. We demonstrate (i) how to set up a real-time movie recommender using the popular MovieLens-100k dataset, and (ii) how to simultaneously support multiple application domains based on the use-case of recommendations in entrepreneurial start-up founding. For that, we differentiate between domains on the item- and system-level. We believe that our demonstration shows a convenient way to adapt, deploy and evaluate a recommender system in an API-centric way. The source-code and documentation that demonstrates how to utilize the configured Uptrendz API is available on GitHub.