Netflix Knows You Better Than Your Cat: A Mind-Blowing Lesson in AI

Netflix is family at this point. In fact, one can even argue that it knows us even better than our families. For example, you are a 20-something woman deciding what to watch after work, and it has a knack for knowing exactly what you’re in the mood for, like a true friend. The range of Netflix recommendations will surprise you as one minute you’re rooting for Kate Hudson to snag Matthew McConaughey in “How to Lose a Guy in 10 Days,” and the next you’re glued to “The Jeffrey Dahmer Story” in your 30s. Talk about a plot twist! Netflix doubles up as your genie with the help of some AI wizardry. The recommendation algorithm is the secret sauce that increases our engagement with the platform. So, let’s take a look at Netflix recommendations, how they are powered by AI, and why they are a gold standard for businesses.

How Does Netflix Use AI to Personalize Recommendations for Users?

Netflix recommendations rely on a combination of machine learning and artificial intelligence techniques. Let’s see how:

1. Content-Based Filtering

The company analyzes the metadata of each TV show or movie such as genre, actors, director, release year, and plot keywords. As a result, Netflix can recommend similar content by understanding the attributes of content that users have rated positively in the past.

2. Matrix Factorization

The platform uses algorithms like Singular Value Decomposition (SVD) to identify hidden patterns in user behavior, among other factors. It helps predict how users will rate new content and can thus help refine Netflix recommendations.

3. Collaborative Filtering

The behavior of similar users helps Netflix machine learning models deliver suggestions to that cohort. For instance, if John and David have similar preferences, then the model can recommend content John has enjoyed to David, and vice versa.

Deep Learning

Deep learning models, such as neural networks, enhance the efficacy of Netflix recommendations. The company can process vast amounts of data and sniff out complex patterns to augment the precision of its recommendations.

ALSO READ: Mastering Deep Learning: The 10 Must-Know Algorithms for AI Professionals in 2024

What Data Points are Considered When Generating Personalized Netflix Recommendations?

Netflix and AI consider a wide range of data points when generating personalized recommendations, including:

1. Explicit User Data

It refers to a set of data provided directly by users. For example, the ratings (like it, love it, and not for me) left on titles are crucial for Netflix machine learning models. Furthermore, browsing history is handy even if one does not watch the title. This is because it influences Netflix recommendations by inferring preferred genres.

2. Implicit User Data

The company tracks viewing habits by collecting everything from what shows and movies one chooses to how long they are watched, especially if one watches them to the end. It paints a detailed picture of user preferences. Moreover, factors like pausing, rewinding, and fast-forwarding can also indicate engagement level.

3. Miscellaneous Data

Netflix and AI consider aspects like the time of the day as well as the day of the week since shows users watch late at night might differ from daytime picks. They track devices to find out where a show is streamed—a phone, laptop, tablet, or TV. Additionally, a user’s account activity indicates whether they watch different content on multiple profiles.

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What are the Benefits and Limitations of Personalized Content Suggestions?

Personalization has various benefits and limitations. They are as follows:

1. Benefits

A. Improves User Experience

It helps drive users to content tailored to their preferences, resulting in a more satisfying experience.

B. Increases Engagement

Personalization enables users to discover new content they are most likely to enjoy, leading to users spending more time on the platform.

C. Enhances Retention

Most companies typically leverage personalization to ensure that users keep coming back to their service and reduce their churn.

D. Improves Discovery

The feature allows companies to shed light on content that might otherwise go unnoticed to maximize the utilization of the platform’s catalog.

2. Limitations

A. Creates Silos

There is a risk that users may inadvertently find themselves in a filter bubble, where users are limited to content that aligns with their existing preferences.

B. Affects Privacy

Most companies train their personalization models by collecting and analyzing user data, raising privacy concerns in the absence of a robust data protection policy.

C. Exhibits Bias

Personalization algorithms may be biased because of factors such as demographics. This might lead to recommendations that reinforce stereotypes or exclude certain groups.

D. Limits Recommendations

Personalization restricts recommendations to users’ past behavior, which may not always reflect their current interests or mood.

ALSO READ: Top 10 AI Skills You Need to Compete in the Digital World

How Can Businesses Leverage AI to Enhance User Experiences?

The contribution of AI to the global economy may climb up to $15.7 trillion in 2030, according to a PwC study. It therefore becomes critical for businesses to reap the gains resulting from the advancement of AI. Here’s why:

1. Boosts Personalization

Firms can analyze user data, such as purchase history and browsing behavior, among other things, to recommend products or services relevant to user preferences. It can also help to make website content, emails, or marketing messages more relevant depending on location or past interactions.

2. Streamlines Customer Service

Many companies are already using AI-powered chatbots to provide 24/7 customer support and resolve basic issues. Moreover, they can use AI to evaluate customer reviews, social media mentions, or support tickets to identify areas for improvement.

3. Augments Efficiency

An organization can delegate repetitive tasks to AI, such as data entry, scheduling, or report generation, and free up employees to focus on strategic tasks. Additionally, AI can schedule maintenance in advance and minimize downtime consequently.

4. Augments Product Development

Every firm must use customer feedback to understand pain points and feature requests and develop relevant products. The use of AI can not only facilitate this process but also note market trends to predict future customer needs, allowing businesses to meet evolving demands.

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What Ethical Considerations Should be Taken Into Account When Developing AI Algorithms for Recommendations?

1. Fairness

Every AI algorithm should be trained on unbiased data to mitigate biases, prevent discrimination, and ensure fair treatment across a wide range of demographic groups. 

2. Privacy

AI algorithms should respect user privacy by collecting relevant data, anonymizing personal information, and providing clear permission mechanisms to share data.

3. Transparency

Transparency stipulates that users should know how algorithms work, what data is used, and why certain content is suggested to them.

4. Accountability

There should be a clear line of responsibility for decisions made by personalized algorithms. In other words, there should be oversight and avenues for recourse in case of errors or harm.

5. Social Impact

Every developer must study the consequences of personalization algorithms by inspecting their potential to influence user behavior, shape cultural norms, and impact democratic processes.

Netflix has revolutionized the industry in the last decade, but we are now entering the era of AI. Every organization is looking to implement AI if they have not done it already. They will need professionals with expertise in different models of AI and their ethical ramifications. Emeritus offers artificial intelligence courses and machine learning courses designed to advance the careers of professionals in the field of AI. These courses offer practical insights into the latest trends by industry experts. Enroll now to unlock your potential in an AI-driven world.

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