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@article{178609,
        author = {Anurag Deep and Harsh Singh and Manish Kumar and Dimple S Maurya},
        title = {House Price Prediction And Recommendation Using CBF And KNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4115-4120},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178609},
        abstract = {The real estate industry is a critical sector of the global economy, influencing urban development, investment strategies, and housing affordability. With the rise of digital technologies and data-driven decision-making, machine learning and artificial intelligence (AI) have become essential tools for property price prediction and recommendation systems. This study explores various methodologies employed in real estate analytics, including regression models, neural networks, and hybrid recommendation techniques. It highlights the integration of map-based recommendation systems, multi-criteria decision- making (MCDM) methods, and fuzzy logic to enhance user experience and improve predictive accuracy. Additionally, the paper examines challenges such as data sparsity, the cold start problem, and the impact of spatial factors on recommendation performance. The findings demonstrate that a hybrid approach combining content-based filtering, collaborative filtering, and geospatial data significantly improves property recommendations. This research provides insights into the evolving landscape of real estate analytics and suggests future directions for optimizing data-driven property valuation and recommendation systems.},
        keywords = {House Price Prediction, Recommendation System, Machine Learning, Real Estate Analytics, Multi-Criteria Decision Making (MCDM), Fuzzy Logic Index Terms—House Recommendation, Machine Learning, CBF, KNN, Price Prediction, SARIMA},
        month = {May},
        }