Comparison of Selected Algorithms in Movie Recommender System
Source abstract
Abstract
Recommender systems are currently very popular, and their main goal is to propose relevant content to users based on various parameters. The main goal of this paper is to create a comprehensive comparison of selected algorithms in movie recommender systems. The recommender system works with the MovieLens database. The main output of the proposed comparison is finding the best algorithm for selecting movies that are the most relevant to user preferences. The paper contains experimental verification of the performance of the proposed algorithms, with an emphasis on their evaluation based on metrics such as Precision, Recall and F1 score. The goal of the evaluation is to assess how well each algorithm performs in generating accurate and relevant recommendations. The testing process includes analysis of the results achieved on the test set of users.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Recommender Systems and Techniques · Image and Video Quality Assessment · Image Retrieval and Classification Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Informatics and Computers, University of Ostrava, 30. dubna 22, 701 03 Ostrava, Czech Republic
Funding
Czech Ministry of Education, Youth and Sports
References (62)
Show source-provided references
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