Venue recommendation approaches have become particularly useful nowadays due to the increasing number of users registered in location-based social networks (LBSNs), applications where it is possible to share the venues someone has visited and establish connections with other users in the system. Besides, the venue recommendation problem has certain characteristics that differ from traditional recommendation, and it can also benefit from other contextual aspects to not only recommend independent venues, but complete routes or venue sequences of related locations. Hence, in this paper, we investigate the problem of route recommendation under the perspective of generating a sequence of meaningful locations for the users, by analyzing both their personal interests and the intrinsic relationships between the venues. We divide this problem into three stages, proposing general solutions to each case: First, we state a general methodology to derive user routes from LBSNs datasets that can be applied in as many scenarios as possible; second, we define a reranking framework that generate sequences of items from recommendation lists using different techniques; and third, we propose an evaluation metric that captures both accuracy and sequentiality at the same time. We report our experiments on several LBSNs datasets and by means of different recommendation quality metrics and algorithms. As a result, we have found that classical recommender systems are comparable to specifically tailored algorithms for this task, although exploiting the temporal dimension, in general, helps on improving the performance of these techniques; additionally, the proposed reranking strategies show promising results in terms of finding a trade-off between relevance, sequentiality, and distance, essential dimensions in both venue and route recommendation tasks.

中文翻译：

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使用序列感知评估将重排序策略应用于路线推荐

由于在基于位置的社交网络 (LBSN) 中注册的用户数量不断增加，场所推荐方法如今变得特别有用，LBSN 应用程序可以共享某人访问过的场所并与系统中的其他用户建立联系。此外，场地推荐问题具有不同于传统推荐的某些特点，它也可以从其他上下文方面受益，不仅可以推荐独立的场地，还可以推荐相关位置的完整路线或场地序列。因此，在本文中，我们通过分析用户的个人兴趣和场地之间的内在关系，在为用户生成一系列有意义的位置的角度下研究路线推荐问题。我们将这个问题分为三个阶段，针对每种情况提出了通用的解决方案：首先，我们陈述了一种从 LBSNs 数据集中导出用户路由的通用方法，该方法可以应用于尽可能多的场景；其次，我们定义了一个重新排序框架，该框架使用不同的技术从推荐列表中生成项目序列；第三，我们提出了一个同时捕获准确性和顺序性的评估指标。我们通过不同的推荐质量指标和算法报告了我们在几个 LBSNs 数据集上的实验。结果，我们发现经典推荐系统可与针对此任务专门定制的算法相媲美，尽管利用时间维度通常有助于提高这些技术的性能；此外，