The emergence of Location-based social networks (LBSNs) in recent years has boosted improvements in Recommender Systems for a new and specific task: the recommendation of points-of-interest (POI). Despite all the blunt advances recently observed, the area still lacks an updated and consolidated view about the main limitations, common assumptions, and directions we are following as a community. Thus, this paper aims to provide an updated picture of POI recommendation, identifying relevant efforts, results, contributions, and limitations. In this sense, through a systematic mapping, we selected 74 relevant papers published in the last three years (2017, 2018, and 2019) in the main conferences and journals of the area. Most of these studies have focused on the general POI recommendation problem, although we still could identify a significant number of efforts addressing specific problems in the area, such as Time-Aware, Next POI, and In/Out-of-Town Recommendations. Also, we found user contexts, such as social network and geolocation, as recurring data types used to improve preference elicitation when exploring different methods (e.g., collaborative filtering, factorization, probabilistic, link-based, and hybrid methods). As major limitations, first, we identified that these studies prioritize accuracy over other quality dimensions, despite the consensus in the RS community that accuracy alone is not enough to assess the practical effectiveness of Recommender Systems. Further, we found a low intersection of metrics and datasets used to evaluate the proposed solutions, along with a large number of metrics used in a few distinct studies. These observations point out the potential damage to reproducibility and straightforward comparison of results in the area, motivating us to propose an extensible POI recommendation benchmark. Through this benchmark, we showed that when proper evaluations are carried out, by considering different datasets, metrics, and baselines, identifying which algorithm is the best one becomes non-trivial. Finally, we highlight as a promising future work the in-depth exploitation of textual data since just a few evaluated studies marginally use this rich data source.

近年来，基于位置的社交网络（LBSN）的出现推动了推荐系统在一项新的特定任务：兴趣点（POI）推荐方面的改进。尽管最近观察到了所有直截了当的进展，但该地区仍缺乏关于我们作为社区所遵循的主要局限性，共同假设和方向的更新和综合的观点。因此，本文旨在提供有关POI建议的最新信息，并确定相关的工作，成果，贡献和局限性。从这个意义上讲，我们通过系统地映射，选择了过去三年（2017年，2018年和2019年）在该地区主要会议和期刊上发表的74篇相关论文。这些研究大多集中在一般POI推荐问题上，尽管我们仍然可以找到大量解决该领域特定问题的方法，例如时间感知，下一个POI和In / Out-of-Town推荐。此外，我们发现用户上下文（例如社交网络和地理位置）是在探索不同方法（例如协作过滤，分解，概率，基于链接的方法和混合方法）时用于改善偏好激发的重复数据类型。作为主要限制，首先，我们确定，尽管RS界一致认为仅凭准确性不足以评估推荐系统的实际有效性，但这些研究将准确性优先于其他质量维度。此外，我们发现用于评估建议解决方案的度量标准和数据集的相交性较低，并且在一些不同的研究中使用了大量的度量标准。这些观察结果指出了对可重复性的潜在损害，并且对该领域的结果进行了直接比较，促使我们提出了可扩展的POI推荐基准。通过该基准，我们表明，在进行适当的评估时，通过考虑不同的数据集，指标和基线，确定哪种算法是最佳算法变得不容易。最后，我们强调对文本数据的深度开发是一项很有前途的未来工作，因为只有少数经过评估的研究很少使用这种丰富的数据源。通过考虑不同的数据集，指标和基线，确定哪种算法是最佳算法变得不容易。最后，我们强调对文本数据的深度开发是一项很有前途的未来工作，因为只有少数经过评估的研究很少使用这种丰富的数据源。通过考虑不同的数据集，指标和基线，确定哪种算法是最佳算法变得不容易。最后，我们强调对文本数据的深度开发是一项很有前途的未来工作，因为只有少数经过评估的研究很少使用这种丰富的数据源。