An Enhanced Framework for Efficient Point-of-Interest Recommendations in Recommender Systems
Subject Areas : Multimedia Processing, Communications Systems, Intelligent SystemsَAli Asghar Salehi soleymanabadi 1 , Keyhan Khamforoosh 2 * , Vafa Maihami 3
1 - PhD Student, Department of Computer Engineering, Sa.C., Islamic Azad University, Sanandaj, Iran
2 - Assistant Professor, Department of Computer Engineering, Sa.C., Islamic Azad University, Sanandaj, Iran
3 - Assistant Professor, Department of Computer Engineering, Sa.C., Islamic Azad University, Sanandaj, Iran
Keywords: Recommender Systems, Points of Interest, Enhanced Framework, Neutrosophic,
Abstract :
Location-based recommender systems significantly enhance user experience in areas such as restaurant recommendations, recreational spaces, and urban services by utilizing spatial data and analyzing user behavior. This research proposes a novel framework to improve the accuracy and quality of recommendations, leveraging machine learning methods and neutrosophic multi-criteria decision modeling. The proposed model consists of three main stages: spatial data processing, recommender system optimization, and final recommendation ranking. Key features, including geographical proximity, user behavioral patterns, and social network data, have been identified to analyze locations more accurately. Clustering algorithms, such as the Imperialist Competitive Algorithm (ICA) and Fuzzy C-Means, are employed to identify geographically close clusters. Additionally, the neutrosophic VIKOR multi-criteria decision-making method is utilized for final ranking within each cluster. Evaluation results using datasets from Yelp, Foursquare, and Flickr indicate that the proposed model achieves higher accuracy compared to traditional methods. A comparison with baseline approaches, including Popularity Rank (PR), Classic Rank (CLR), and Frequent Rank (FR), demonstrates improved performance in terms of accuracy, mean absolute error, and user acceptance rate of recommendations. Furthermore, comparisons with other studies reveal that incorporating more comprehensive data, such as visit time and weather conditions, along with more detailed data analysis, enhances the quality of recommendations. Finally, a comparison of the proposed method with the CPRNS and MMPOI algorithms, both of which introduced a framework, shows that the implemented improvements achieve better performance in terms of Precision, Recall, and NDCG2 metrics.
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