Uptrendz: API-Centric Real-time Recommendations in Multi-Domain Settings
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.
Keywords
Cite
@article{arxiv.2301.01037,
title = {Uptrendz: API-Centric Real-time Recommendations in Multi-Domain Settings},
author = {Emanuel Lacic and Tomislav Duricic and Leon Fadljevic and Dieter Theiler and Dominik Kowald},
journal= {arXiv preprint arXiv:2301.01037},
year = {2023}
}
Comments
ECIR 2023 demo paper