User Preference Mining in Real Estate: Techniques and Applications
This paper explores user preference mining techniques in real estate, utilizing statistical methods and machine learning models like XGBoost, LSTM, and Seq4Rec to address challenges in high-dimensional, sparse data, enhancing personalized recommendations and marketing strategies.
This paper presents a comprehensive analysis of user preference mining in real estate, focusing on statistical and machine learning approaches. It discusses challenges such as high-dimensional sparse data and multi-peak preferences, proposing solutions like Seq4Rec and deep interest networks. The study emphasizes model interpretability and practical applications in personalized recommendations and targeted marketing.
Key techniques include time-series modeling with LSTM, embedding-based item representation, and attention mechanisms to capture complex user behaviors. The research also evaluates model performance through validation metrics like inner product and cross-entropy, demonstrating improvements in preference prediction accuracy.
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