Recommendation systems have lately been popularized globally, with primary use cases in online interaction systems, with significant focus on e-commerce platforms. We have developed a machine learning-based recommendation platform, which can be easily applied to almost any items and/or actions domain. Contrary to existing recommendation systems, our platform supports multiple types of interaction data with multiple modalities of metadata natively. This is achieved through multi-modal fusion of various data representations. We deployed the platform into multiple e-commerce stores of different kinds, e.g. food and beverages, shoes, fashion items, telecom operators. Here, we present our system, its flexibility and performance. We also show benchmark results on open datasets, that significantly outperform state-of-the-art prior work.
@article{arxiv.2005.06331,
title = {Multi-modal Embedding Fusion-based Recommender},
author = {Anna Wroblewska and Jacek Dabrowski and Michal Pastuszak and Andrzej Michalowski and Michal Daniluk and Barbara Rychalska and Mikolaj Wieczorek and Sylwia Sysko-Romanczuk},
journal= {arXiv preprint arXiv:2005.06331},
year = {2022}
}