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Injection of Knowledge in a Sourcing Recommender System

Molka Tounsi Dhouib 1 Catherine Faron 1 Andrea Tettamanzi 1
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : Recommender systems provide suggestions to users for items that best meet their needs. In this work, we study the benefits of using knowledge and, more specifically, a 'bag of concepts' representation to enhance a recommender system in the sourcing domain. We tested our approach in a real-world case study provided by the Silex company. The experimental results show that injecting knowledge in the recommendation process outperforms word embedding approaches.
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Submitted on : Monday, November 9, 2020 - 4:15:17 PM
Last modification on : Tuesday, December 7, 2021 - 4:10:19 PM
Long-term archiving on: : Wednesday, February 10, 2021 - 7:27:30 PM


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  • HAL Id : hal-02996442, version 1



Molka Tounsi Dhouib, Catherine Faron, Andrea Tettamanzi. Injection of Knowledge in a Sourcing Recommender System. WI-IAT'20 - IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, Dec 2020, Melbourne / Virtual, Australia. ⟨hal-02996442⟩



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