Multi-Modal Feature Analysis for User Intent Prediction: A Framework for Enhanced Look-to-Book Ratio in Digital Platforms

Authors

Anirudh Reddy Pathe

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

Multi-Modal Feature Analysis for User Intent Prediction: A Framework for Enhanced Look-to-Book Ratio in Digital Platforms. Anirudh Reddy Pathe. 2019. IJIRCT, Volume 5, Issue 1. Pages 1-10. https://www.ijirct.org/viewPaper.php?paperId=2412011