article · Intelligent and Converged Networks
Online search for movies across vast digital catalogues often proves tedious and inefficient, as viewers struggle to find content aligned with their shifting preferences. To address this challenge and lessen user effort, recommendation systems utilise collaborative filtering techniques to predict suitable choices. Four distinct sparse matrix completion approaches are evaluated and compared: k-nearest neighbours, matrix factorisation, co-clustering, and slope-one. These algorithms are implemented to form a predictive classification model designed to assist users in discovering films. System performance across the evaluated models is measured and compared using the root mean square error scale, providing insight into how cooperative filtering can effectively handle large movie datasets.
Digital streaming and media platforms host massive catalogues that overwhelm consumers trying to select relevant entertainment. Recommendation systems help streamline this discovery process by anticipating user preferences. Comparing established collaborative filtering and sparse matrix completion algorithms helps clarify effective approaches for predicting user taste and reducing time spent searching large databases.
The research applies directly to movie streaming services, online entertainment platforms, and content distribution networks looking to improve user retention by streamlining media discovery. Given that the work focuses on evaluating standard algorithmic approaches using root mean square error rather than reporting on a production deployment, the technology appears to be early-stage or benchmark research requiring further integration into real-world platform architectures.
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Online search has become very popular, and users can easily search for any movie title; however, to easily search for moving titles, users have to select a title that suits their taste. Otherwise, people will have difficulty choosing the film they want to watch. The process of choosing or searching for a film in a large film database is currently time-consuming and tedious. Users spend extensive time on the internet or on several movie viewing sites without success until they find a film that matches their taste. This happens especially because humans are confused about choosing things and quickly change their minds. Hence, the recommendation system becomes critical. This study aims to reduce user effort and facilitate the movie research task. Further, we used the root mean square error scale to evaluate and compare different models adopted in this paper. These models were employed with the aim of developing a classification model for predicting movies. Thus, we tested and evaluated several cooperative filtering techniques. We used four approaches to implement sparse matrix completion algorithms: <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -nearest neighbors, matrix factorization, co-clustering, and slope-one.
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DOI: 10.23919/icn.2023.0024
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