Multi-Modal Feature Analysis for User Intent Prediction: A Framework for Enhanced Look-to-Book Ratio in Digital Platforms
Author(s): Anirudh Reddy Pathe
Publication #: 2412011
Date of Publication: 09.01.2019
Country: USA
Pages: 1-10
Published In: Volume 5 Issue 1 January-2019
DOI: https://doi.org/10.5281/zenodo.14282008
Abstract
This research introduces an innovative framework for predicting user intent through multi-modal feature analysis, specifically designed to enhance look-to-book ratios in digital platforms. We present a comprehensive approach that leverages advanced machine learning techniques to process and analyze visual, textual, and behavioral data streams simultaneously. The framework incorporates novel feature fusion mechanisms and adaptive learning strategies to improve prediction accuracy while maintaining computational efficiency. Our theoretical analysis demonstrates the framework's potential for significant improvements in user intent prediction and conversion rate optimization, with particular emphasis on scalability and real-time processing capabilities.
Keywords: Multi-Modal Analysis, Feature Fusion, Deep Learning, User Intent Prediction, Look-To-Book Ratio, Neural Networks, Behavioral Analytics, Conversion Optimization, Attention Mechanisms, Temporal Modeling
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