Learning To Rank for E commerce Cart Optimization

Murali Mohana Krishna Dandu; Siddhey Mahadik,; Prof.(Dr.) Arpit Jain; Md Abul Khair; Om Goel

doi:10.36676/urr.v10.i2.1372

Authors

Murali Mohana Krishna Dandu Independent Researcher, Satyanarayana Puram, Vijayawada, Andhra Pradesh 520011.
Siddhey Mahadik, Independent Researcher, Vashi, Navi Mumbai, Maharashtra, India,
Prof.(Dr.) Arpit Jain Independent Researcher KL University, Vijaywada, Andhra Pradesh,
Md Abul Khair Independent Researcher, Sikkim Manipal University, Sikkim, India,
Om Goel Independent Researcher, Abes Engineering College Ghaziabad,

DOI:

https://doi.org/10.36676/urr.v10.i2.1372

Keywords:

Learning to Rank, e-commerce cart optimization, product recommendation, machine learning, user behaviour analysis, conversion rate improvement

Abstract

In the competitive landscape of e-commerce, optimizing the shopping cart experience is crucial for enhancing customer satisfaction and boosting conversion rates. This paper explores the application of Learning to Rank (LTR) techniques to optimize e-commerce cart functionality. Learning to Rank, a subset of machine learning, involves training models to order items or features in a way that maximizes relevance based on user preferences. By employing LTR algorithms, e-commerce platforms can improve the relevance of product recommendations, streamline the checkout process, and tailor user interactions based on historical data and user behaviour.

The study outlines the integration of LTR models into e-commerce systems, focusing on how these models can predict and prioritize products that users are more likely to purchase. This involves analysing user interactions, purchase history, and product attributes to develop ranking models that enhance the cart's efficiency and effectiveness. The research also addresses challenges such as data sparsity, dynamic user preferences, and the need for real-time updates, offering solutions and best practices for implementing LTR in practical settings.

The results demonstrate that applying LTR techniques can significantly improve cart optimization by presenting users with more relevant product options, reducing cart abandonment rates, and increasing overall sales. This paper provides a comprehensive framework for e-commerce businesses seeking to leverage LTR for cart optimization, highlighting both theoretical insights and practical implications.

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