In the personalized Point-Of-Interest (POI) (or venue) recommendation, the diversity of recommended POIs is an important aspect. Diversity is especially important when POIs are recommended in the target users’ frequently visited areas, because users are likely to revisit such areas. In addition to the (POI) category diversity that is a popular diversification objective in recommendation domains, diversification of recommended POI locations is an interesting subject itself. Despite its importance, existing POI recommender studies generally focus on and evaluate prediction accuracy. In this article, geographical diversification ( geo-diversification ), a novel diversification concept that aims to increase recommendation coverage for a target users’ geographic areas of interest, is introduced, from which a method that improves geo-diversity as an addition to existing state-of-the-art POI recommenders is proposed. In experiments with the datasets from two real Location Based Social Networks (LSBNs), we first analyze the performance of four state-of-the-art POI recommenders from various evaluation perspectives including category diversity and geo-diversity that have not been examined previously. The proposed method consistently improves geo-diversity (CPR(geo)@20) by 5 to 12% when combined with four state-of-the-art POI recommenders with negligible prediction accuracy (Recall@20) loss and provides 6 to 18% geo-diversity improvement with tolerable prediction accuracy loss (up to 2.4%).

中文翻译：

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常去地区推荐兴趣点的地理多样化

在个性化兴趣点（POI）（或地点）推荐中，推荐兴趣点的多样性是一个重要方面。当在目标用户经常访问的区域推荐 POI 时，多样性尤其重要，因为用户可能会重新访问这些区域。除了（POI）类别多样性是推荐领域中流行的多样化目标之外，推荐 POI 位置的多样化本身就是一个有趣的主题。尽管它很重要，但现有的 POI 推荐器研究通常关注和评估预测准确性。在本文中，地域多样化（地域多元化)，一种新颖的多样化概念，旨在增加对目标用户感兴趣的地理区域的推荐覆盖率，从中提出了一种改进地理多样性的方法，作为现有最先进的 POI 推荐器的补充. 在对来自两个真实的基于位置的社交网络 (LSBN) 的数据集进行的实验中，我们首先从各种评估角度分析了四个最先进的 POI 推荐器的性能，包括以前没有检查过的类别多样性和地理多样性。当与四个最先进的 POI 推荐器结合使用时，所提出的方法持续将地理多样性 (CPR(geo)@20) 提高 5% 到 12%，而预测准确度 (Recall@20) 损失可忽略不计，并提供 6% 到 18%地理多样性改善与可容忍的预测精度损失（高达 2.4%）。