Injection of Knowledge in a Sourcing Recommender System
Overview & Synopsis
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.
Indexed from repository: HAL Open Science
(
hal.science)
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Document Information
| Document Title | Injection of Knowledge in a Sourcing Recommender System |
|---|---|
| Author / Organization | Molka Tounsi Dhouib, Catherine Faron, Andrea G. B. Tettamanzi |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2020 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-231493 |
| Original Source Link | Visit Source Page on hal.science » |