Imagine a world where your streaming service predicts your mood, knows your favourite actors and remembers the films you watched last month. This is already the reality of personalised streaming experiences powered by AI. In today’s content-overloaded world, personalisation has become key to unlocking user satisfaction and loyalty. Audiences are no longer content with generic recommendations. They want selective experiences that reflect their unique tastes.

Introduction to AI in streaming

Did you know that Netflix attributes up to 80% of viewer activity to personalised recommendations powered by artificial intelligence? In fact, AI-driven suggestions save Netflix an estimated $1 billion annually in customer retention. This staggering statistic underlines how much of a role AI-based solutions play in shaping our viewing habits and improving the overall user experience.

Streaming services have come a long way since their beginnings. Platforms are constantly innovating, offering high-quality video, original content and user-friendly interfaces. But even with these advances, a key question remains – how do we find what we want to watch in this ever-evolving universe of content? This is where personalisation enters the scene.

Today, simply offering a large library is not enough. Users want selective experiences that satisfy their individual preferences. Delivering personalised experiences is a must to retain viewers and stand out in a crowded market.

The huge potential of artificial intelligence to analyse user preferences and predict future behaviour has made it ideal for delivering personalised streaming experiences. AI encompasses a range of technologies, including machine learning algorithms, natural language processing (NLP) and computer vision, which together form the basis of today’s recommendation systems.

How AI is transforming content recommendations

Artificial intelligence (AI) is rapidly changing the way streaming services recommend content, moving beyond basic algorithms towards a more strategic and user-centric approach. This shift marks a significant step forward, offering tangible benefits for both companies and viewers.

Overview of recommendation engines and their importance

Recommendation engines have become a critical tool for businesses of all sizes. These intelligent systems act as digital filters, sifting through vast amounts of data to surface the most relevant items for individual users.

How recommendation engines work:

Recommendation engines leverage a combination of data and algorithms to deliver targeted suggestions. Here is a breakdown of the key components:

Data collection: Recommendation engines gather information about user behaviour. This can include past purchases, browsing history, search queries, ratings, and even clicks.

Analysis and modeling: Advanced algorithms analyse this data to identify patterns and preferences.

Recommendation generation: Based on the analysis, the engine generates personalised recommendations for each user.

Machine learning algorithms

Recommendation engines rely on the power of machine learning algorithms to analyse data and generate personalised suggestions. These algorithms operate in fascinating ways, each with its own strengths and applications. Let’s delve into some of the most common machine learning algorithms used in recommendation systems:

Collaborative filtering:

User-based collaborative filtering: Suggests content liked by users with similar tastes.

Item-based collaborative filtering: Recommends content like what a user has already watched and enjoyed.

Content-based filtering:

Uses metadata and attributes of watched content to recommend similar titles.

Example: Recommending action films to a viewer who frequently watches them.

Hybrid models

Combine collaborative and content-based filtering to deliver more accurate recommendations.

Example: Netflix’s recommendation system uses a hybrid model.

Deep learning algorithms:

Employ neural networks for advanced recommendation tasks.

Natural Language Processing (NLP) and metadata analysis

NLP allows computers to understand the meaning behind human language. In the field of recommendations, NLP analyses things like your film reviews, searching for keywords, sentiment (positive or negative), and even the tone of your writing. This helps build a richer picture of your tastes.

Metadata is ‘data about data’. In the case of films, this can include genre, director, actors and even plot summaries. By analysing the metadata, the system can identify links between seemingly disparate films.

NLP and metadata analysis work together to create a powerful recommendation engine.

Computer vision

Computer vision technologies enrich recommendation systems by analysing visual elements of video content in various ways.

Content-based image retrieval analyses video frames to identify visual patterns and classify content. This approach allows streaming platforms to group visually similar shows or movies, enabling them to provide more relevant recommendations based on visual similarities.

Video thumbnails and previews optimise thumbnail selection based on viewer preferences. By using A/B testing, platforms identify which previews attract the most clicks, thereby improving user engagement and driving up viewership.

Scene and object recognition recognises specific scenes or objects that are popular with viewers. For instance, it can identify iconic scenes and use this information to recommend shows with similar scenes, ensuring that viewers are directed toward content that aligns with their preferences.

Success stories: leading platforms leveraging AI for personalised streaming recommendations

Artificial intelligence has become an indispensable tool for streaming platforms seeking to improve user engagement and satisfaction. Here are some examples of how some of the world’s most popular platforms are successfully using AI to deliver personalised recommendations and improve the viewer experience.

Netflix

Netflix is a pioneer in personalised streaming recommendations. What are they using?

Hybrid recommendation model: Netflix’s algorithm merges collaborative filtering, content-based filtering, and deep learning to deliver accurate recommendations.

A/B testing: They conduct over 250 A/B tests annually to optimise the recommendation engine and user interface.

Thumbnails and previews: Netflix uses computer vision to tailor thumbnails and previews to individual preferences, increasing click-through rates.

source: https://medium.com/@narengowda/netflix-system-design-dbec30fede8d

Prime Video

Prime Video leverages AI to improve content recommendations by analysing user behaviour and reviews, delivering a highly personalised viewing experience. They benefit from:

NLP-powered reviews: They use Natural Language Processing (NLP) to analyse customer reviews, sentiment, and ratings to refine content suggestions.

Collaborative filtering: Suggests new content based on the viewing habits of similar users.

Personalised carousels: Customises homepage carousels based on viewing history and preferences.

source: https://www.t3.com/news/amazon-prime-video-icon-movie-channel-2

YouTube

YouTube employs deep learning algorithms to curate personalised recommendations, optimising the viewer journey from one video to the next. They are using:

Deep neural networks: Analyses user watch history, search queries, and engagement patterns to deliver relevant video suggestions.

User feedback loop: Continuously learns from user interactions (likes, shares, watch duration) to refine the recommendation engine.

Channel recommendations: Suggests new channels to subscribe to, based on user interests and previously watched videos.

source: https://medium.com/@shengyuchen/demystifying-youtube-recommendations-from-a-users-point-of-view-75a8888df33c

Conclusion

The era of endless scrolling and content fatigue is coming to an end. Artificial intelligence-based recommendations are ushering in a new era of personalised discovery, tailoring the content you watch to your unique preferences and ever-changing tastes. So next time you log in, give AI recommendations a try. You might just discover your next favourite show!

At Spyrosoft BSG, we specialise in AI-driven media solutions designed to help streaming platforms enhance their recommendations and increase viewer engagement. Whether you aim to refine your recommendation engine, develop smarter search functions, or personalise your user interface, our expertise can help you achieve your goals.

Get in touch with us today to discover how our AI-driven solutions can transform your media business!