With the advent of the Internet of Things, especially the Internet of Vehicles, abundant environmental and mobile data can be generated continuously. A personalized recommender system is one of the important methods for solving the problem of big data overload. However, to make use of these mobile data from vehicles, traditional recommender services are confronted by severe challenges. Therefore, we study the diversified recommendation problem based on a real-world dataset, represented as a tensor with three dimensions of user, location and activity. As the tensor is rather sparse, we employ tensor decomposition to predict missing values. Additionally, we directly regard recommendation precision as an objective. In addition to precision, we also consider the recommendation novelty and coverage, providing a more comprehensive view of the recommender system. Thus, visitors can discover attractive spots that are less visited in a personalized manner, relieving traffic pressure at famous scenic spots and balancing overall transportation. By integrating all these objectives, we construct a many-objective recommendation model. To optimize this model, we propose a distributed parallel evolutionary algorithm employing the nondominated ranking and crowding distance. Compared with the state-of-the-art algorithms, the proposed algorithm performs well and is very efficient.

中文翻译：

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基于移动数据的多元化个性化推荐优化

随着物联网（尤其是车辆互联网）的出现，可以连续生成大量的环境和移动数据。个性化推荐系统是解决大数据过载问题的重要方法之一。然而，为了利用来自车辆的这些移动数据，传统的推荐服务面临着严峻的挑战。因此，我们基于真实数据集研究多样化的推荐问题，该数据集表示为具有用户，位置和活动三个维度的张量。由于张量相当稀疏，因此我们使用张量分解来预测缺失值。此外，我们直接将推荐精度作为目标。除了精确度外，我们还考虑了推荐的新颖性和覆盖范围，提供更全面的推荐系统视图。因此，游客可以发现个性化方式较少参观的景点，从而减轻了著名景点的交通压力并平衡了整体交通。通过整合所有这些目标，我们构建了一个多目标推荐模型。为了优化该模型，我们提出了一种使用非支配排名和拥挤距离的分布式并行进化算法。与最新算法相比，该算法性能良好，效率很高。为了优化该模型，我们提出了一种使用非支配排名和拥挤距离的分布式并行进化算法。与最新算法相比，该算法性能良好，效率很高。为了优化该模型，我们提出了一种使用非支配排名和拥挤距离的分布式并行进化算法。与最新算法相比，该算法性能良好，效率很高。