Deep Multi-View Learning for Tire Recommendation

TL;DR

This paper explores the application of multi-view learning models to enhance recommender systems by effectively integrating diverse data sources, demonstrating improved performance through a comparative study on industrial data.

Contribution

It presents a comparative analysis of state-of-the-art multi-view models applied to recommender systems, highlighting their effectiveness in handling multi-view data.

Findings

Multi-view learning improves recommendation accuracy.

Certain models outperform others in industrial data contexts.

Multi-view integration enhances user profiling capabilities.

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.