Deep Multi-View Learning for Tire Recommendation

2021/07/18 by Thomas Ranvier, Kilian Bourhis, Khalid Benabdeslem +1 · 2 citations
Computer Science · #Advanced Image and Video Retrieval Techniques #Artificial intelligence #Computer science #Data science #Image Retrieval and Classification Techniques #Machine learning #Order (exchange) #Recommender Systems and Techniques #Recommender system #Relevance (law) #cs.IR #cs.LG

paper · pdf · doi:10.1109/ijcnn52387.2021.9534318

published as 2021 International Joint Conference on Neural Networks (IJCNN), Jul 2021, Shenzhen, China. pp.1-8

openalex publication_date 2021/07/18 · arxiv created 2022/03/23 · arxiv updated 2022/03/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

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.

Citations

• A Survey on Multi-view Learning
• Neural Machine Translation by Jointly Learning to Align and Translate
• Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions