AI and Content Recommendation

Saïd Business School via Coursera

Go to class Write review

Overview

Google, IBM & Meta Certificates — All 10,000+ Courses at 40% Off

One annual plan covers every course and certificate on Coursera. 40% off for a limited time.

Get Full Access

Build your subject-matter expertise This course is part of the AI in Media specialisation. When you enrol in this course; you'll also be enrolled in this Specialisation. ● Learn new concepts from industry experts ● Gain a foundational understanding of modern strategic tools ● Develop job-relevant skills with hands-on scenario-based projects ● Earn a shareable career certificate About this Course In a world where content is tailored to users, understanding recommendation algorithms is vital for any media professional. This course explores how organisations use machine learning to build 'models of you', predicting tastes in real time to maximise engagement and customer lifetime value. Through real-world examples such as Netflix and YouTube, you will discover how supervised and reinforcement learning drive the social feeds and streaming menus we use every day. You will also examine the societal risks of these systems, such as radicalisation and filter bubbles, and explore the 'algotorial' approach, where human editorial judgement balances algorithmic efficiency. Whether you are a marketer, producer, or strategist, this course provides the essential foundations of the modern media landscape. What you'll learn • Analyse the impact of artificial intelligence on the media value chain, specifically evaluating how recommendation algorithms function to maximise audience engagement and retention. • Apply core machine learning concepts—including content-based and collaborative filtering—to real-world media scenarios, while formulating strategies to mitigate ethical risks such as filter bubbles and algorithmic radicalisation. • Assess the emerging importance of AI optimisation and generative AI reputation, developing approaches for organisations to manage their visibility within large language models.

Syllabus

Specialisation Introduction: AI in Media

This specialisation equips media professionals and business leaders with a comprehensive understanding of how AI is transforming the media industry. Across three interconnected courses, you'll explore the recommendation algorithms powering platforms like Netflix and YouTube, examine the capabilities and limitations of generative AI tools and develop practical strategies for integrating AI into media workflows responsibly. You'll critically assess both opportunities and risks, including copyright complexities, bias mitigation, disinformation threats and compliance requirements. Designed at Oxford Saïd Business School, this series prepares you to navigate the evolving media landscape, build AI-informed strategies and harness these technologies to drive innovation whilst maintaining ethical and legal standards. Please note that this introductory module is common to all courses in the AI in Media specialisation. If you have already studied the 'AI and Creativity' or 'AI and Production' courses, you can skip this section, unless you find a recap useful.

Course Introduction: AI and Content Recommendation

Artificial intelligence is fundamentally reshaping the media landscape, shifting the paradigm from audiences searching for content to content searching for audiences. In this course, you'll explore the sophisticated algorithms driving global platforms like Netflix, YouTube and Spotify, unpacking the mechanics of supervised, unsupervised and reinforcement learning. You'll also examine the critical challenges of algorithmic bias and 'rabbit holes', alongside strategies for reputation management and 'Answer Engine Optimisation' in an age of generative AI.

Machine Learning in Media

Machine learning is the engine room of modern media, dictating how content is discovered, consumed and monetised. In this module, you will deconstruct the three fundamental methodologies that drive recommendation algorithms: supervised, unsupervised and reinforcement learning. By examining real-world applications from platforms such as Spotify, Netflix and TikTok, you will learn how 'agents' are trained to maximise engagement, how hidden patterns are discovered in raw data and how organisations balance the need to explore new content against the need to exploit known user preferences.

Practical Dimensions of Content Recommendation

Moving beyond the theoretical concepts of machine learning, this module examines how major media organisations practically deploy recommendation engines to define the modern user experience. You will explore the specific mechanics of content-based and collaborative filtering, understanding how algorithms determine what content appears in a user’s feed. Through deep-dive case studies of industry leaders like Netflix and YouTube, you will analyse how these systems are engineered to maximise 'stickiness', reduce churn and optimise Customer Lifetime Value using a mix of explicit and implicit data signals.

Managing Risk in Content Recommendation

While recommendation algorithms are powerful engines for engagement, they carry significant risks when optimised solely for attention. In this module, you'll examine the 'rabbit hole' phenomenon, where users are inadvertently led towards extreme content or trapped in filter bubbles. You'll explore the trade-off between algorithmic efficiency and societal well-being and discover the 'algotorial' approach. This hybrid strategy combines the speed of machine learning with human editorial judgement, offering organisations a practical framework to mitigate risk, ensure diversity and maintain safety in content distribution.

Optimising Upside and Minimising Downside in Content Recommendation

In the modern media landscape, an organisation's reputation is increasingly defined not just by general internet sentiment, but by the 'opinions' of large language models (LLMs). This module explores the strategic shift from traditional search engine optimisation to Answer Engine Optimisation (AEO). You will examine how recommendation algorithms perceive brands, the impact of AI inference on public standing, and the practical application of data science in content creation to master the algorithms of platforms like YouTube and Spotify.

Course Conclusion

This final module consolidates your learning from across the course, summarising the core mechanics of supervised, unsupervised and reinforcement learning, the practical applications of content and collaborative filtering used by platforms like Netflix, Spotify and YouTube, the ethical risks of the 'rabbit hole' effect and strategies for reputation management and creator optimisation in algorithmic systems. Finally, you will apply your knowledge through a peer-reviewed assignment that challenges you to develop a strategic recommendation for how a streaming service could adopt algorithmic recommendation systems, and to analyse the societal impacts of content recommendation algorithms, including the risk of radicalisation.