Restaurant recommendation is one of the most recommendation problems because the result of recommendation varies in different environments. Many methods have been proposed to recommend restaurants in a mobile environment by considering user preference, restaurant attributes, and location. However, there are few restaurant recommender systems according to the internet of vehicles environment. This paper presents a recommender system based on the prediction of traffic conditions in the internet of vehicles environment. This recommender system uses a phased selection method to recommend restaurants. The first stage is to screen restaurants that are on the user’s driving route; the second stage is to recommend restaurants from the user attributes, restaurant attributes (with traffic conditions), and vehicle context, using a deep learning model. The experimental evaluation shows that the proposed recommender system is both efficient and effective.

中文翻译：

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基于交通状况预测的车辆上下文餐厅推荐

餐厅推荐是最常见的推荐问题之一，因为不同环境的推荐结果不同。已经提出了许多通过考虑用户偏好、餐厅属性和位置来推荐移动环境中的餐厅的方法。然而，很少有针对车联网环境的餐厅推荐系统。本文提出了一种基于车联网环境中交通状况预测的推荐系统。该推荐系统使用分阶段的选择方法来推荐餐厅。第一阶段是筛选用户驾车路线上的餐厅；第二阶段是使用深度学习模型从用户属性、餐厅属性（有交通状况）和车辆上下文中推荐餐厅。