Poster:
Video:
Reflection:
The process of creating my video explainer on Netflix’s recommendation system was a great journey that made me think of how to effectively engage an audience while communicating a key concern of the studio. My goal was to explore how Netflix uses AI and automated decision-making (ADM) to recommend content as well as how viewers discover new movies today compared to before recommendation systems existed. During the studio exhibition, I observed that attendees were most intrigued by Netflix’s use of personalized thumbnails and the subtle ways AI influences what we choose to watch. I drew heavily from the Netflix Tech Blog’s article on “Artwork Personalization,” which explained how Netflix customizes visuals for each user to increase engagement (Maharajan, 2022). The audience’s reaction confirmed that this concept resonated well, showing how effective the integration of real-world examples was in communicating the broader implications of AI on media consumption. This remind the idea that simplifying complex AI mechanisms while making them relatable was the best way to engage viewers.
In terms of what worked well in my process, the most successful aspect was integrating expert ideas from my interview with Professor Danula Hettiachchi. His insights on Netflix’s algorithm, especially regarding collaborative filtering and the impact of shared accounts added a level of credibility and depth that significantly enriched the content. His knowledge helped clarify complex technical points which make the video more authoritative. However, the most problematic aspect of my process occurred during the production phase, where I faced a technical issue with my piece to camera footage. After rushing through the filming, I later realized that none of the footage had sound, which caused considerable stress given the time constraints. This setback was due to poor preparation as I did not check the equipment beforehand. Despite the panic, I managed to re record the footage and complete the project on time. This experience taught me the importance of time management and technical diligence, which are essential for any media project.
If I were to continue working on this piece, there are several areas I would focus on improving and extending. One key area for improvement would be expanding the discussion on algorithmic bias and content diversity within Netflix’s recommendation system. While I briefly touched on this topic, there is much more to explore about how algorithms can reinforce certain content over others, potentially limiting exposure to diverse or niche media. Drawing from research such as “Platform Studies and Netflix” (Leaver, 2022), I would extend this analysis to critically examine how AI shapes cultural consumption and the ethical concerns around it. Additionally, I would include more visual aids and animations in the video to explain technical concepts like collaborative filtering. Visual elements would make the explanation of these algorithms clearer and more engaging. Finally, I would include vox pops or testimonials from Netflix users to provide personal perspectives on how they interact with the platform’s recommendation system. This would add a human element to the video which make it more relatable to a wider audience.
One of the most valuable lessons I took from this studio experience is the importance of detailed planning and time management. Throughout the project, I often found myself struggling to keep up with deadlines due to rushed preparation. This resulted in avoidable errors such as the sound issue with my footage. Moving forward, I plan to implement stricter timelines for all stages of production, allowing time for revisions, equipment checks and troubleshooting. This would not only reduce stress but also improve the overall quality of my work. Another key takeaway from this experience is the value of collaboration. My interview with Professor Hettiachchi was instrumental in refining my ideas and adding depth to the project. His expertise in AI and recommendation systems provided insights that enhanced the credibility and accuracy of my video. This reinforced the idea that seeking out expert input or collaborating with others is crucial, especially when dealing with complex topics. Collaborative efforts can elevate the quality of a project by introducing new perspectives and knowledge that may otherwise be overlooked.
In terms of readings that influenced my approach, the Netflix Tech Blog article on artwork personalization (Maharajan, 2022) played a crucial role in shaping the section of the video that dealt with dynamic thumbnails. The article explained how Netflix uses machine learning to generate personalized artwork for each user, based on their viewing habits. This helped me explain the ways in which Netflix tailors its homepage to engage users more effectively. Additionally, the YouTube video about Netflix (Netflix Research: Recommendations) from week 7, which broke down the technical aspects of collaborative filtering and AI, was really invaluable in helping me structure the explanation of Netflix’s recommendation engine (Netflix Technology, 2022). This source provided clear visual examples of how algorithms use user data to make personalized suggestions, which I mirrored in my own video. Lastly, the academic article on platform studies (Leaver, 2022) provided a broader context for my discussion on how Netflix, as a platform, influences media consumption. This article helped me explore the ethical implications of AI-driven recommendations, particularly in terms of content diversity and algorithmic bias.
In conclusion, the process of creating this video explainer taught me valuable lessons about audience engagement, technical production, and the importance of collaboration. By focusing on simplifying complex AI concepts and making them relatable, I was able to effectively communicate the broader concerns of the studio. The setbacks I encountered during production emphasized the need for better time management, while the success of integrating expert insights demonstrated the value of collaboration. If I were to extend this project, I would focus on further exploring the ethical implications of AI in media and enhancing the visual storytelling elements of the video to make technical concepts more accessible to a wider audience.
References:
Leaver, T. (2022). Platform studies and Netflix: Algorithms, branded genres, and artificial intelligence. Television & New Media, 23(5), 524-539. https://doi.org/10.1177/15274764221102864
Maharajan, A. (2022, September 7). Artwork personalization at Netflix. Netflix Tech Blog. https://netflixtechblog.com/artwork-personalization-c589f074ad76
Netflix Technology. (2022, September 9). How Netflix recommends TV shows and movies using AI | AI vs. Machine Learning [Video]. YouTube. https://www.youtube.com/watch?v=4aNfPe-pQqI