{'fraction_non_alphanumeric': 0.03610675039246468, 'fraction_numerical': 0.01098901098901099, 'mean_word_length': 5.100478468899522, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Survey on Point-of-Interest Recommendation in Location-based Social Networks

The popularization of Location-based social networks (LBSNs) in last years has provided a lot of improvements in several Recommender Systems to the task of points-of-interest (POI) recommendation. In this paper, we provide an updated view of the POI recommendation, identifying relevant efforts, results, contributions, and limitations. Through a systematic mapping, we selected 73 relevant papers published in the last three years (2017, 2018, and 2019) in the main vehicles of the area (e.g., RecSys, VLDB, SIGIR, WWW, TKDE, etc.). As major limitations, first, we identified that these works prioritize accuracy over other quality dimensions, despite the consensus in the RS community that accuracy is not enough to assess the practical effectiveness of RSs. Further, we found a low intersection of metrics and datasets used in these works, along with a large number of metrics used in a few distinct studies. These observations show restrictions for reproducibility and straightforward comparison of results in the area. Finally, we highlight as a promising future work the in-depth exploitation of textual data, since just a few of the evaluated papers marginally use this rich data source.