Empowering Recommendations with NLP: Exploiting Textual Reviews for Enhanced Rating-Based Systems

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Published: Oct 7, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i10s.7596
Keywords:
Natural language processing, sentiment analysis, word embeddings, recommender system, rating prediction, collaborative filtering

Main Article Content

Mohd Danish
Department of Computer Engineering Jamia Millia Islamia New Delhi, India.
Mohammad Amjad
Department of Computer Engineering Jamia Millia Islamia New Delhi, India

Abstract

This research paper proposes a rating-based recommender system that leverages Natural Language Processing (NLP) techniques to enhance the accuracy and effectiveness of recommendations. Traditional recommender systems primarily rely on numerical ratings provided by users to make predictions. However, these ratings often lack detailed information about user preferences and suffer from sparsity and inconsistency issues. By incorporating NLP, we aim to extract valuable insights from textual reviews and improve the recommendation process. Our system utilizes sentiment analysis, topic modelling, and text embeddings to capture the implicit information in reviews and generate more personalized and context-aware recommendations. The experimental results demonstrate the superior performance of the proposed rating-based recommender system compared to conventional approaches.

Article Details

How to Cite
Danish, M. ., & Amjad, M. . (2023). Empowering Recommendations with NLP: Exploiting Textual Reviews for Enhanced Rating-Based Systems. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10s), 64–68. https://doi.org/10.17762/ijritcc.v11i10s.7596
Issue
Vol. 11 No. 10s (2023)
Section
Articles

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