@inproceedings{509e583b61324c03889201a97fbe396a,
title = "Matchmaking Engine for Energy Data Marketplace Using Word Embedding Techniques",
abstract = "This work presents an approach for a matchmaking engine in the energy data marketplace. The engine uses a hybrid recommender system that combines knowledge-based and content-based filtering to suggest the best data assets to users. The matchmaking service utilizes annotated data to identify the most suitable match between data consumers' needs and available data in the energy marketplace. The recommended datasets are ordered by their relevance to the user's needs. To accomplish this, the matchmaking engine employs two popular word embedding techniques: TF-IDF and word2vec, for content-based filtering. Additionally, the knowledge-based recommender leverages the semantic annotation of data obtained from mapping data to a data model.",
keywords = "Data Modeling, Energy Data, Matchmaking Engine, Recommender System",
author = "Negin Mehrbod and Ruben Costa and Ahmad Mehrbod and Mansoor Ahmed and Fenareti Lampathaki",
note = "Funding Information: ACKNOWLEDGMENT The authors acknowledge the European Commission for the support and funding under the scope of Horizon2020 SYNERGY Project (Grant Agreement Number 872734) and the partners of the SYNERGY Project Consortium. Publisher Copyright: {\textcopyright} 2023 IEEE.; 29th International Conference on Engineering, Technology, and Innovation, ICE 2023 ; Conference date: 19-06-2023 Through 22-06-2023",
year = "2023",
doi = "10.1109/ICE/ITMC58018.2023.10332327",
language = "English",
isbn = "979-8-3503-1518-9",
series = "IEEE International Conference on Engineering, Technology and Innovation",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
booktitle = "Proceedings of the 29th International Conference on Engineering, Technology, and Innovation",
address = "United States",
}
