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July 11, 2022Mobile Information SystemsOpen Access

Rating-Based Recommender System Based on Textual Reviews Using IoT Smart Devices

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Authors

MAMuqeem AhmedMaulana Azad National Urdu UniversityMAMohd Dilshad AnsariSRM UniversityNSNinni SinghMadan Mohan Malaviya University of Technology

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Overview

Experimental study demonstrates enhanced recommender accuracy using user-based collaborative filtering on movie reviews, indicating improved automated decision-making across IoT devices.

Key Points

  • The study aims to develop and evaluate an enhanced rating-based recommender system that analyzes textual reviews and user behavior for IoT-connected platforms.
  • Implemented a user-based collaborative filtering algorithm (CFA) applied to textual review datasets in the movie domain.
  • Conducted modeling, performance evaluation, and comparative benchmarking using the RapidMiner Java-based data analytics platform.
  • Demonstrated higher classification precision and operational efficiency relative to conventional recommendation algorithms.
  • Achieved lower statistical error rates when processing user-generated text reviews for real-time recommendation contexts.

Cite This Study

Ahmed et al. (2022) studied this question.

synapsesocial.com/papers/6a08dcd634cfc5f8bc5b6d7ahttps://doi.org/10.1155/2022/2854741
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