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Deep Multi-View Learning for Tire Recommendation


We are constantly using recommender systems, often without even noticing. They build a profile of our person in order to recommend the content we will most likely be interested in. The data representing the users, their interactions with the system or the products may come from different sources and be of a various nature. Our goal is to use a multi-view learning approach to improve our recommender system and improve its capacity to manage multi-view data. We propose a comparative study between several state-of-the-art multi-view models applied to our industrial data. Our study demonstrates the relevance of using multi-view learning within recommender systems.
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hal-03614780 , version 1 (22-03-2022)



Thomas Ranvier, Kilian Bourhis, Khalid Benabdeslem, Bruno Canitia. Deep Multi-View Learning for Tire Recommendation. 2021 International Joint Conference on Neural Networks (IJCNN), Jul 2021, Shenzhen, China. pp.1-8, ⟨10.1109/IJCNN52387.2021.9534318⟩. ⟨hal-03614780⟩
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