A location recommender in social networks based on location based on deep learning

1403 4 62 86behavior, 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.