Multi-modal Embedding Fusion-based Recommender

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ID: 282982
2020
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Abstract
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.
Reference Key
sysko-romanczuk2020multimodal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Anna Wroblewska; Jacek Dabrowski; Michal Pastuszak; Andrzej Michalowski; Michal Daniluk; Barbara Rychalska; Mikolaj Wieczorek; Sylwia Sysko-Romanczuk
Journal arXiv
Year 2020
DOI
DOI not found
URL
http://arxiv.org/abs/2005.06331v2
Keywords
cs.lg cs.ir cs.cv

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