Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems

Abstract

Expert assessments with pre-defined numerical or language terms can limit the scope of decision-making models. We propose that decision-making models can incorporate expert judgments expressed in natural language through sentiment analysis. To help make more informed choices, we present the Sentiment Analysis in Recommender Systems with Multi-person, Multi-criteria Decision Making (SAR-MCMD) method. This method compiles the opinions of several experts by analyzing their written reviews and, if applicable, their star ratings. The growth of online applications and the sheer amount of available information have made it difficult for users to decide which information or products to select from the Internet. Intelligent decision-support technologies, known as recommender systems, leverage users’ preferences to suggest what they might find interesting. Recommender systems are one of the many approaches to dealing with information overload issues. These systems have traditionally relied on single-grading algorithms to predict and communicate users’ opinions for observed items. To boost their predictive and recommendation abilities, multi-criteria recommender systems assign numerous ratings to various qualities of products. We created, manually annotated, and released the technique in a case study of restaurant selection using ‘TripAdvisor reviews’, ‘TMDB 5000 movies’, and an ‘Amazon dataset’. In various areas, cutting-edge deep learning approaches have led to breakthrough progress. Recently, researchers have begun to focus on applying these methods to recommendation systems, and different deep learning-based recommendation models have been suggested. Due to its proficiency with sparse data in large data systems and its ability to construct complex models that characterize user performance for the recommended procedure, deep learning is a formidable tool. In this article, we introduce a model for a multi-criteria recommender system that combines the best of both deep learning and multi-criteria decision-making. According to our findings, the suggested system may give customers very accurate suggestions with a sentiment analysis accuracy of 98%. Additionally, the metrics, accuracy, precision, recall, and F1 score are where the system truly shines, much above what has been achieved in the past.

A Recommendation System for Prosumers Based on Large Language Models

“…As for the decision-making models, integrating expert judgments expressed in natural language via sentiment analysis may enrich decision-making processes. The sentiment analysis in recommender systems with multi-person, multi-criteria decision making method leveraged written expert reviews and ratings to inform decisions, addressing the challenge of information overload through intelligent recommender systems [33]. These systems, traditionally based on single-grading algorithms, were enhanced by multi-criteria systems that evaluate various product aspects.…”

“…No [33] Present SAR-MCMD method for incorporating expert judgments into recommender systems through sentiment analysis.…”

A Recommendation System for Prosumers Based on Large Language Models

“…No [42] Present SAR-MCMD method for incorporating expert judgments into recommender systems through sentiment analysis.…”

“…As for the decision-making models, integrating expert judgments expressed in natural language via sentiment analysis may enrich decision-making processes. The sentiment analysis in recommender systems with multi-person, multi-criteria decision-making methods leveraged written expert reviews and ratings to inform decisions, addressing the challenge of information overload through intelligent recommender systems [ 42 ]. These systems, traditionally based on single-grading algorithms, were enhanced by multi-criteria systems that evaluate various product aspects.…”