Proposing a Semantic Movie Recommendation System Enhanced by ChatGPT's NLP Results

TL;DR

This paper introduces a novel semantic movie recommendation system that leverages ChatGPT's NLP capabilities to analyze movie descriptions and improve recommendation accuracy beyond traditional genre-based methods.

Contribution

The study presents a new approach to building a knowledge graph using ChatGPT for semantic analysis of movie descriptions, enhancing recommendation precision.

Findings

Semantic analysis improves recommendation accuracy

ChatGPT-based method outperforms genre-based suggestions

Enhanced user satisfaction through personalized recommendations

Abstract

The importance of recommender systems on the web has grown, especially in the movie industry, with a vast selection of options to watch. To assist users in traversing available items and finding relevant results, recommender systems analyze operational data and investigate users' tastes and habits. Providing highly individualized suggestions can boost user engagement and satisfaction, which is one of the fundamental goals of the movie industry, significantly in online platforms. According to recent studies and research, using knowledge-based techniques and considering the semantic ideas of the textual data is a suitable way to get more appropriate results. This study provides a new method for building a knowledge graph based on semantic information. It uses the ChatGPT, as a large language model, to assess the brief descriptions of movies and extract their tone of voice. Results indicated that using the proposed method may significantly enhance accuracy rather than employing the explicit genres supplied by the publishers.