Electronics | |
Designing Multi-Modal Embedding Fusion-Based Recommender | |
Sylwia Sysko-Romańczuk1  Andrzej Michałowski2  Michał Daniluk2  Anna Wróblewska2  Jacek Dąbrowski2  Barbara Rychalska2  Mikołaj Wieczorek2  Michał Pastuszak2  | |
[1] Faculty of Management, Warsaw University of Technology, Narbutta 85, 02-524 Warsaw, Poland;Synerise S.A., Giełdowa 1, 01-211 Warsaw, Poland; | |
关键词: recommendations; machine learning; deep learning; multi-modal representation; data representation; embeddings; | |
DOI : 10.3390/electronics11091391 | |
来源: DOAJ |
【 摘 要 】
Recommendation systems have lately been popularised globally. However, often they need to be adapted to particular data and the use case. We have developed a machine learning-based recommendation system, which can be easily applied to almost any items and/or actions domain. Contrary to existing recommendation systems, our system supports multiple types of interaction data with various modalities of metadata through a multi-modal fusion of different data representations. We deployed the system into numerous e-commerce stores, e.g., food and beverages, shoes, fashion items, and telecom operators. We present our system and its main algorithms for data representations and multi-modal fusion. We show benchmark results on open datasets that outperform the state-of-the-art prior work. We also demonstrate use cases for different e-commerce sites.
【 授权许可】
Unknown