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JSDP 2025, 21(4): 85-96 |
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rastgoo M, Ghaffari H R. A location recommender in social networks based on location based on deep learning. JSDP 2025; 21 (4) : 6
URL: http://jsdp.rcisp.ac.ir/article-1-1365-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1365-en.html
Ferdows Branch Azad University & Assistant Professor, Faculty of Computer Engineering, Ferdous Branch, Islamic Azad University, Ferdous, Iran
Abstract: (337 Views)
The potential of social networks to extract valuable insights into user behavior has become a focal point of research. With the proliferation of social media platforms, people are increasingly sharing their experiences online. This wealth of user-generated data provides unique opportunities to understand movement patterns and predict future behavior. Location-based social networks like Foursquare exemplify this, allowing users to check in at various locations and enabling researchers to analyze these data points.By analyzing the data collected from these platforms, we can uncover patterns in user behavior, such as frequently visited locations and the factors influencing these choices. This information can be invaluable for businesses and urban planners.To improve the accuracy of predicting a user's next location, this study focuses on identifying the most influential friends or individuals in a user's social network. Factors such as the strength of these relationships, historical visit data, and temporal-spatial characteristics are considered. Additionally, the study emphasizes the importance of data quality, focusing on locations that have been visited more than 100 times to ensure reliability.
A key aspect of this research is understanding the influence of social connections on individual behavior. By analyzing the overlap in visited locations between friends, the study aims to identify the most influential friends for each user. These influential friends are then used to predict the user's next location.
The proposed method employs machine learning techniques, specifically RandomForest and recurrent neural networks (LSTM, RNN, and GRU), to predict user behavior. RandomForest is used to analyze the data and identify the most significant features, while recurrent neural networks are employed to model the sequential nature of user behavior. Among these, LSTM achieved the highest accuracy of 71% in predicting users' next locations.This research demonstrates that combining artificial intelligence with spatial-temporal data can provide profound insights into human behavior in urban and digital environments. By understanding these patterns, businesses can tailor their offerings to individual customers, and urban planners can design more efficient and user-friendly cities.
Article number: 6
Type of Study: Research |
Subject:
Paper
Received: 2023/03/17 | Accepted: 2024/12/4 | Published: 2025/04/2 | ePublished: 2025/04/2
Received: 2023/03/17 | Accepted: 2024/12/4 | Published: 2025/04/2 | ePublished: 2025/04/2
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