Summary:
Point-of-Interest recommendation is an area of increasing research and development interest within the widely adopted technologies known as Recommender Systems. Among them, those that exploit information coming from Location-Based Social Networks are very popular nowadays and could work with different information sources, which pose several challenges and research questions to the community as a whole. We present a systematic review focused on the research done over the past 10 years about this topic. We discuss and categorize the algorithms and evaluation methodologies used in these works and point out the opportunities and challenges that remain open in the field. More specifically, we report on the leading recommendation techniques and information sources that have been exploited more often (such as the geographical signal and deep learning approaches) while we also examine the lack of reproducibility in the field that may hinder real performance improvements.
JCR Impact Factor and WoS quartile: 16,600 - Q1 (2022); 23,800 - Q1 (2023)
DOI reference:
https://doi.org/10.1145/3510409
Published on paper: January 2022.
Published on-line: September 2022.
Citation:
P. Sánchez, A. Bellogín, Point-of-interest recommender systems based on location-based social networks: a survey from an experimental perspective. ACM Computing Surveys. Vol. 54, nº. 11s, pp. 223-1 - 223-37, January 2022. [Online: September 2022]
