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| Title | Point-Of-Interests Recommendation Service In Location-Based Social Networks: A Survey, Research Challenges, And Future Perspectives |
|---|---|
| ID_Doc | 42270 |
| Authors | Asaad S.M.; Ghafoor K.Z.; Sarhang H.; Mulahuwaish A. |
| Year | 2023 |
| Published | Studies in Computational Intelligence, 942 |
| DOI | http://dx.doi.org/10.1007/978-3-031-08815-5_4 |
| Abstract | The focus on accurate Point-Of-Interest (POI) recommendation, specifically in location-based services (LBS), has gained all social-network developers’ attention. This is because the POI service has a significant role in helping users to locate targeted areas, including hospitals, airports, stations, billing addresses, post-office, shopping-mall, and other POIs. Equally, many attempts have been realized to provide accurate POI recommendation solutions via commercial and academic sectors. However, the recommendation solutions have their weaknesses and abilities in terms of initial check-ins, accuracy, behavior of the users’ activities, and historical passed locations. According to the state-of-the-art, a survey of such solutions and utilized techniques is needed. Therefore, this paper aims to address most of the currently proposed solutions and implemented techniques for offering accurate POI recommendation systems. Further, this paper also presents a taxonomy of POI recommendation solutions in which the solutions are classified into content-based filtering, collaborative-based filtering, and hybrid-based filtering solutions. This is with a particular focus on the details of the implemented techniques/algorithms and utilized features. Providing an accurate POIs recommendation solution and other related issues are listed as future research attempts. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
| Author Keywords | Location-based social networks (LBSNs; POI recommendation; Point of interest (POI); Recommendations systems; Smart city |
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