Integrating BiLSTM and CNN for predicting user mobility from geotagged social media posts

Abstract

Recommender systems for social media platforms face significant challenges in accurately capturing user preferences from unstructured geotagged data. This research introduces a hybrid recommendation system leveraging Convolutional neural networks (CNNs) paired with Bidirectional Long Short-Term Memory (BiLSTM) networks to improve the prediction of user mobility and preferences. The proposed model calculates user similarity by analyzing opinions and preferences extracted from social media posts, combining CNN strength in feature extraction with BiLSTM ability to capture users dependencies. By incorporating demographic data, the system addresses the cold-start issue and improves recommendation accuracy by utilizing contextual information. Experimental results using datasets from Yelp and Flickr demonstrate significant advancements in RMSE, F-Score, MAP, and NDCG metrics. These findings highlight the effectiveness of the CNN-BiLSTM hybrid approach in generating personalized, sentiment-aware, and contextually rich recommendations on social media platforms.