Yumytech presents its research results in California
We are very proud to announce that our research collaboration with the University of Geneva is bearing fruit. Our collaborator Kévin Guyard, was in California on September 19, at the IEEE “ai4i” conference in Los Angeles to present the promising results of his research on hybrid machine learning models.
It is often difficult to automate the adaptation of the various stages of a recommendation system when it is scaled up. Indeed, the more users you have, the more precise you are, but the calculation times become exponential. Therefore, the challenge was to find a trade-off to remain extremely accurate while keeping computation times low. The system is intended to be autonomous in its adjustment procedures.
This hybrid model will have a practical application for AI-based matchmaking in our European research project WisdomOfAge. We invite you to discover this future digital platform that takes advantage of the experience and background of people over 50 years old, allowing them to share their knowledge and solve specific problems of industrial companies.
For more information on this recommendation system, we invite you to discover the paper of Kévin Guyard’s conference below.
A scalable recommendation system approach for a companies – seniors matching
Abstract—Recommendation systems are becoming more and more present in our daily lives, whether it is for suggesting items to buy, movies to watch or music to listen. They can be used in a large number of contexts. In this paper, we propose the use of a recommendation system in the context of a recruitment platform. The use of the recommendation system allows to obtain precise profile recommendations based on the competences of a candidate to meet the stated requirements and to avoid companies to have to perform a very time-consuming manual sorting of the candidates. Thus, this paper presents the context in which we propose this recommendation system, the data preprocessing, the general approach based on a hybrid Content-Based Filtering (CBF) and Similarity Index (SI) system, as well as the means implemented to reduce the computational cost of such a system with the increasing evolution of the platform.
Index Terms—Artificial intelligence, machine learning, recruitment, hybrid recommendation system, collaborative filtering, similarity index.