Multi-modal Embedding Fusion-based Recommender

• URL: http://arxiv.org/abs/2005.06331v2
• Date: Thu, 14 May 2020 11:45:22 GMT
• Title: Multi-modal Embedding Fusion-based Recommender
• Authors: Anna Wroblewska (1 and 2), Jacek Dabrowski (1), Michal Pastuszak (1), Andrzej Michalowski (1), Michal Daniluk (1), Barbara Rychalska (1 and 2), Mikolaj Wieczorek (1), Sylwia Sysko-Romanczuk (2) ((1) Synerise, (2) Warsaw University of Technology)
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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