Deep Multi-View Learning for Tire Recommendation
- URL: http://arxiv.org/abs/2203.12451v1
- Date: Wed, 23 Mar 2022 14:43:14 GMT
- Title: Deep Multi-View Learning for Tire Recommendation
- Authors: Thomas Ranvier, Kilian Bourhis, Khalid Benabdeslem, Bruno Canitia
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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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