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Injection of Knowledge in a Sourcing Recommender System

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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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Dates and versions

hal-02996442 , version 1 (09-11-2020)

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  • HAL Id : hal-02996442 , version 1

Cite

Molka Tounsi Dhouib, Catherine Faron, Andrea G. B. 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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