Building a movie recommender system

• Titel: Building a movie recommender system
• Mitwirkende: Schrooten, Bernd [MitwirkendeR]
• Institutionen: Packt Publishing, [Verlag]
• Ausgabe: [First edition].
• Verlagsort: Birmingham, UK
• Verlag: Packt Publishing Ltd.
• Jahr: 2025
• Umfang: 1 online resource (1 video file (44 min.))
• Illustrationen: sound, color.
• Fussnoten: "DataLab" : DataLab is a learning solution designed to empower individuals and teams to explore data, build skills, and solve real-world problems. With interactive content, hands-on exercises, and a focus on practical application, DataLab helps learners turn data curiosity into confident capability—no matter where they start. - Online resource; title from title details screen (O’Reilly, viewed April 10, 2025)
• ISBN: 978-1-80602-923-5
• ISBN: 1-80602-923-5
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Systèmes de recommandation (Filtrage d'information)
• Sach-SW: Apprentissage automatique
• Sach-SW: Intelligence artificielle ; Logiciels
• Form-SW: Instructional films
• Form-SW: Nonfiction films
• Form-SW: Internet videos
• Form-SW: Films de formation
• Form-SW: Films autres que de fiction
• Form-SW: Vidéos sur Internet
• K10plus-PPN: 1924459507
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4718959616
• Lokale URL Inst.: Zum Volltext

Abstract

Recommendation engines are the backbone of personalized user experiences, helping users discover content tailored to their preferences. In this code-along, we'll build an AI-powered movie recommendation tool, combining data science, machine learning, and AI-assisted coding. You'll start by exploring how recommendation systems work, analyzing movie metadata and user ratings to uncover patterns. We'll cover essential techniques like collaborative filtering and content-based recommendations to create personalized movie suggestions. Along the way, AI-assisted coding tools will help accelerate development, making it easier to implement complex algorithms. By the end of this session, you'll have a functional recommender system that not only enhances user experiences but also deepens your understanding of real-world AI applications. Whether you're a data enthusiast or an aspiring machine learning practitioner, this project offers valuable hands-on experience in building intelligent, data-driven applications.