Nowadays, recommender systems play an important role in several Location-Based Social Networks (LBSNs). The current advances have considered the trade-off between accuracy and diversity to help users to discover and explore new points-of-interest (POI). However, differently from traditional recommendation scenarios, other equally relevant dimensions (e.g., social and geographical user information) have to be considered to understand how the characteristics of services offered by each POI fit the user needs. Specifically, this work sheds light upon naive failures introduced by traditional recommendation methods while they handle this trade-off between diversity and accuracy in POI recommendations. We hypothesize that some efforts on POI recommendations somehow are deviating from basic learnings from the area. In this context, this work addresses four characteristics inherent to the POI domain that previous efforts have failed to recognize: (1) POI categories and locations are complementary dimensions of diversification that should be simultaneously addressed; (2) Diversity is a complex concept that should be modeled by distinct and non-orthogonal models; (3) Distinct users have different biases and willingness to move to fulfill their needs; (4) POI recommendation is a multi-objective task. In order to demonstrate the gains of properly addressing these aspects, we also propose DisCovER, a straightforward re-ordering method that linearly combines geographical and categorical diversification. DisCovER results demonstrate that even simple strategies to exploit simultaneously these complementary dimensions can increase diversification while keeping accuracy high. Differently from state-of-the-art diversification methods, DisCovER does not penalize any quality dimension in favor of others. It allows us to discuss future directions towards more robust user modeling and preference elicitation in POI domains.

如今，推荐系统在多个基于位置的社交网络（LBSN）中扮演着重要角色。当前的进展已经考虑了准确性和多样性之间的权衡，以帮助用户发现和探索新的兴趣点（POI）。但是，与传统推荐方案不同，必须考虑其他同等重要的维度（例如，社会和地理用户信息）以了解每个POI提供的服务的特征如何满足用户需求。具体而言，这项工作揭示了传统推荐方法在处理POI建议的多样性和准确性之间的这种权衡时会引入的幼稚失败。我们假设关于POI建议的一些努力与该领域的基础知识有所不同。在这种情况下，这项工作解决了POI领域固有的四个特征，而先前的努力未能认识到这些特征：（1）POI类别和位置是多元化的互补维度，应同时解决；（2）多样性是一个复杂的概念，应通过不同且非正交的模型来建模；（3）不同的用户有不同的偏见和意愿来满足他们的需求；（4）POI推荐是一个多目标任务。为了证明正确解决这些方面的好处，我们还建议 （2）多样性是一个复杂的概念，应通过不同且非正交的模型来建模；（3）不同的用户有不同的偏见和意愿来满足他们的需求；（4）POI推荐是一个多目标任务。为了证明正确解决这些方面的好处，我们还建议 （2）多样性是一个复杂的概念，应通过不同且非正交的模型来建模；（3）不同的用户有不同的偏见和意愿来满足他们的需求；（4）POI推荐是一个多目标任务。为了证明正确解决这些方面的好处，我们还建议DisCovER，一种简单的重新排序方法，将地理和类别多样化线性地组合在一起。DisCovER结果表明，即使是同时利用这些互补维度的简单策略，也可以提高多样性，同时保持较高的准确性。与最先进的多元化方法不同，DisCovER不会以任何质量方面的不利于其他方面的代价。它使我们能够讨论在POI域中朝更健壮的用户建模和偏好启发的未来方向。