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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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https://hal.inria.fr/hal-02996442
Contributor : Molka Tounsi Dhouib <>
Submitted on : Monday, November 9, 2020 - 4:15:17 PM
Last modification on : Tuesday, November 10, 2020 - 3:32:09 AM

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Molka Tounsi Dhouib, Catherine Faron, Andrea Tettamanzi. Injection of Knowledge in a Sourcing Recommender System. Web Intelligence, Dec 2020, Melbourne, Australia. ⟨hal-02996442⟩

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