Enhanced group recommendation system: A hybrid context-aware approach with collaborative filtering for location-based social networks
In recent years, location-based social networks (LBSNs) have gained significant popularity, enabling users to interact with points of interest (POIs) using modern technologies. As more people rely on LBSNs for finding interesting venues, contextually aware and relevant recommendation systems have become very beneficial with practical applications. In this research, we propose an enhanced hybrid recommendation system, designed for LBSNs to improve the accuracy of suggestions by integrating collaborative filtering methods with singular value decomposition to handle sparse data, along with context-aware modeling to tailor recommendations based on user interests, and group recommendation to accommodate multi-user scenarios. In addition, we incorporate contextual aspects, such as spatial proximity and temporal behavior, into the model to ensure recommendations align closely with the user’s present surroundings and preferences. The proposed method extends further to group recommendations by considering individual inclinations into cohesive suggestions for groups interested in visiting POIs together. The proposed method is assessed using precision, recall, and F1 score, ensuring a thorough evaluation of its performance. To further highlight context-aware recommendations, we use clustering based on user preference, temporal behavior, and category-wise interaction to identify patterns across various venue types. The proposed method shows improved recommendations, specifically based on data from LBSNs, and develops an efficient solution for balanced user preferences with contextual influences.
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