SEAGET: Seasonal and active hours guided graph enhanced transformer for the next POI recommendation
One of the most important challenges for improving personalized services in industries like tourism is predicting users’ near-future movements based on prior behavior and current circumstances. Next POI (Point of Interest) recommendation is essential for helping users and service providers by provid...
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Elsevier
2025-07-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590005625000128 |
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| author | Alif Al Hasan Md. Musfique Anwar |
| author_facet | Alif Al Hasan Md. Musfique Anwar |
| author_sort | Alif Al Hasan |
| collection | DOAJ |
| description | One of the most important challenges for improving personalized services in industries like tourism is predicting users’ near-future movements based on prior behavior and current circumstances. Next POI (Point of Interest) recommendation is essential for helping users and service providers by providing personalized recommendations. The intricacy of this work, however, stems from the requirement to take into consideration several variables at once, such as user preferences, time contexts, and geographic locations. POI selection is also greatly influenced by elements like a POI’s operational status during desired visit times, desirability for visiting during particular seasons, and its dynamic popularity over time. POI popularity is mostly determined by check-in frequency in recent studies, ignoring visitor volumes, operational constraints, and temporal dynamics. These restrictions result in recommendations that are less than ideal and do not take into account actual circumstances. We propose the Seasonal and Active hours-guided Graph-Enhanced Transformer (SEAGET) model as a solution to these problems. By integrating variations in the seasons, operational status, and temporal dynamics into a graph-enhanced transformer framework, SEAGET capitalizes on redefined POI popularity. This invention gives more accurate and context-aware next POI predictions, with potential applications for optimizing tourist experiences and enhancing location-based services in the tourism industry. |
| format | Article |
| id | doaj-art-fdb2fb1d96604f38bb6abdbf1da48dde |
| institution | OA Journals |
| issn | 2590-0056 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Array |
| spelling | doaj-art-fdb2fb1d96604f38bb6abdbf1da48dde2025-08-20T02:36:58ZengElsevierArray2590-00562025-07-012610038510.1016/j.array.2025.100385SEAGET: Seasonal and active hours guided graph enhanced transformer for the next POI recommendationAlif Al Hasan0Md. Musfique Anwar1Corresponding author.; Department of Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka, BangladeshDepartment of Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka, BangladeshOne of the most important challenges for improving personalized services in industries like tourism is predicting users’ near-future movements based on prior behavior and current circumstances. Next POI (Point of Interest) recommendation is essential for helping users and service providers by providing personalized recommendations. The intricacy of this work, however, stems from the requirement to take into consideration several variables at once, such as user preferences, time contexts, and geographic locations. POI selection is also greatly influenced by elements like a POI’s operational status during desired visit times, desirability for visiting during particular seasons, and its dynamic popularity over time. POI popularity is mostly determined by check-in frequency in recent studies, ignoring visitor volumes, operational constraints, and temporal dynamics. These restrictions result in recommendations that are less than ideal and do not take into account actual circumstances. We propose the Seasonal and Active hours-guided Graph-Enhanced Transformer (SEAGET) model as a solution to these problems. By integrating variations in the seasons, operational status, and temporal dynamics into a graph-enhanced transformer framework, SEAGET capitalizes on redefined POI popularity. This invention gives more accurate and context-aware next POI predictions, with potential applications for optimizing tourist experiences and enhancing location-based services in the tourism industry.http://www.sciencedirect.com/science/article/pii/S2590005625000128PopularitySeasonal influenceOperational timeframeNext POI recommendationTransformerGraph neural networks |
| spellingShingle | Alif Al Hasan Md. Musfique Anwar SEAGET: Seasonal and active hours guided graph enhanced transformer for the next POI recommendation Array Popularity Seasonal influence Operational timeframe Next POI recommendation Transformer Graph neural networks |
| title | SEAGET: Seasonal and active hours guided graph enhanced transformer for the next POI recommendation |
| title_full | SEAGET: Seasonal and active hours guided graph enhanced transformer for the next POI recommendation |
| title_fullStr | SEAGET: Seasonal and active hours guided graph enhanced transformer for the next POI recommendation |
| title_full_unstemmed | SEAGET: Seasonal and active hours guided graph enhanced transformer for the next POI recommendation |
| title_short | SEAGET: Seasonal and active hours guided graph enhanced transformer for the next POI recommendation |
| title_sort | seaget seasonal and active hours guided graph enhanced transformer for the next poi recommendation |
| topic | Popularity Seasonal influence Operational timeframe Next POI recommendation Transformer Graph neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S2590005625000128 |
| work_keys_str_mv | AT alifalhasan seagetseasonalandactivehoursguidedgraphenhancedtransformerforthenextpoirecommendation AT mdmusfiqueanwar seagetseasonalandactivehoursguidedgraphenhancedtransformerforthenextpoirecommendation |