论文标题
深度多视图学习轮胎建议
Deep Multi-View Learning for Tire Recommendation
论文作者
论文摘要
我们一直在使用推荐系统，通常甚至没有注意到。他们建立了我们人的个人资料，以推荐我们最有可能感兴趣的内容。代表用户，他们与系统的交互的数据，或者产品可能来自不同的来源，并且具有各种性质。我们的目标是使用多视图学习方法来改善我们的建议系统并提高其管理多视图数据的能力。我们提出了应用于我们工业数据的几个最先进的多视图模型之间的比较研究。我们的研究证明了在建议系统中使用多视图学习的相关性。
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.
