Author(s)

Viet Hung Nguyen¹, Reeva Valentine^2*

Affiliation(s)

¹School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia.

²School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia.

Corresponding Author

Reeva Valentine

ABSTRACT

This paper explores the optimization of legal recommendation systems through the application of hybrid deep learning approaches. As the volume of legal information continues to grow, traditional methods of legal research have become inadequate, necessitating the integration of advanced technologies to improve efficiency and accuracy in document retrieval. The proposed hybrid framework combines Convolutional Neural Networks, Recurrent Neural Networks , and Transformers to enhance the personalization and relevance of recommendations for legal professionals. The findings indicate that this hybrid model significantly outperforms traditional keyword-based systems by providing context-aware and nuanced recommendations, ultimately aiding legal practitioners in navigating vast repositories of legal documents more effectively. The implications for legal practice are profound, as the framework can automate document analysis, allowing professionals to focus on strategic tasks. Future research directions include expanding the diversity of training data, enhancing user feedback mechanisms, and exploring the explainability of AI-driven recommendations in legal contexts.

KEYWORDS

Legal recommendation systems; Hybrid deep learning; Document analysis

CITE THIS PAPER

Viet Hung Nguyen, Reeva Valentine. Optimizing legal recommendation systems with hybrid deep learning approaches. World Journal of Sociology and Law. 2024, 2(3): 11-20. DOI: https://doi.org/10.61784/wjsl3007.

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