Exploration vs. Exploitation: Finding a Balance
My role: PM, Researcher, AI Focused Product Designer
Company: Netflix (An MIT Project for AI Certification)
Year: 2024
Getting the balance right between exploration and exploitation is what makes a recommendation system feel intuitive, exciting, and just right for the user. It’s like knowing when to suggest a favorite meal and when to introduce a new dish you’re confident they’ll love. If you lean too heavily in either direction, the experience can become frustrating or monotonous.
EXPLORATION
Thrill of the new
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It is all about helping users discover new content and hidden gems that they might not typically seek out. But if it’s pushed too much, users might feel overwhelmed or alienated by unfamiliar recommendations.
EXPLOITATION
Comfort of the familiar
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Focuses on giving users more of what they already enjoy. While this keeps them happy in the short term, it can quickly lead to boredom if the content starts feeling too predictable.
CONTENT DISCOVERY
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People often don’t know what they want until it’s presented to them. An ideal blend of favorites and new genres lets users stumble upon unexpected hits they didn’t realize they’d love, keeping their interest fresh and their curiosity alive.
LONG-TERM ENGAGEMENT
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As users’ tastes evolve, the system should grow with them. A good balance ensures the recommendations keep up, gradually nudging them toward new interests without completely abandoning their current favorites.
THE CONSEQUENCES OF GETTING IT WRONG
Over-Exploitation ➜ Content Fatigue
Think of a user who loves action movies. If Netflix keeps recommending nothing but Action Thrillers and Action Comedies, that once-excited user will eventually get tired and start skipping titles.
Over-Exploration ➜ User Confusion
The same action-loving user suddenly starts seeing Documentaries and Art House Films. It’s jarring and feels off. They might abandon the platform altogether because it no longer reflects their tastes.
DATA DRIVEN EXAMPLES
Metrics
Weeks
Completion Rate and Watch Time steadily decline due to repetitive recommendations.
Skip Rate increases, indicating content fatigue.
Annotation: "High skip rate due to content fatigue" shows where the user starts disengaging.
Metrics
Weeks
Completion Rate drops sharply, and Skip Rate rises due to overly diverse and unexpected recommendations.
Watch Time also decreases, signalling a loss of interest.
Annotation: "Sharp drop due to genre confusion" highlights where too much novelty caused disengagement.
Metrics
Weeks
Stable or improving trends across all metrics due to a good mix of familiar and new content.
Annotation: "Steady engagement due to balanced content" points to the consistent satisfaction.
KEY METRICS
For a recommendation system to really succeed, it needs to keep a close eye on the right metrics. These data points aren’t just numbers—they’re signals that show whether the system is truly getting to know its users, keeping them happy, helping them discover new favorites, and ultimately making them want to come back for more. Tracking these insights is how you keep the balance between satisfaction, surprise, and long-term loyalty.
LEADING METRICS
​Time to First Click​
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Measures the time it takes for a user to make their first click on a recommended title after landing on the homepage.
A shorter time to first click indicates that the recommendations are quickly grabbing the user’s attention and appear highly relevant.
The RL system should aim to reduce this time over sessions, signalling that it’s learning user preferences.
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Watch Time
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The amount of time users spend browsing recommended content before choosing to watch, skip, or exit the platform.
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High dwell time may indicate indecision or dissatisfaction with recommendations, while very low dwell time could mean users are not considering their options carefully.
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The optimal dwell time is moderate, reflecting engaged exploration without overwhelming the user.
Content Variety Exposure​
Tracks how many distinct genres or sub-genres a user engages with over a given period.​​
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A broader variety exposure suggests successful exploration, while very narrow exposure points to over-exploitation.​
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Steady exposure to a mix of core and novel genres, signalling a balanced approach.
Dwell Time
The amount of time users spend browsing recommended content before choosing to watch, skip, or exit the platform.
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High dwell time may indicate indecision or dissatisfaction with recommendations, while very low dwell time could mean users are not considering their options carefully.
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The optimal dwell time is moderate, reflecting engaged exploration without overwhelming the user.
LAGGING METRICS
User Retention​
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Measures the percentage of users who continue using the platform over a defined period (e.g., weekly, monthly).
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Retention is a key indicator of long-term satisfaction and platform loyalty.
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High and stable retention rates over multiple periods, indicating users find value in the recommendations and stay engaged.
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User Satisfaction Scores​
Measures user-reported satisfaction levels with content recommendations, typically collected via surveys or in-app ratings.​
Direct feedback that highlights user perceptions of the recommendation quality.
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Increasing satisfaction scores over time, showing that users are pleased with the content being suggested.
Subscription Renewal Rate
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Percentage of users who renew their subscription after a given time (e.g., annually or monthly).​
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Reflects the overall value users perceive from the platform and its content recommendations.​
Steady or growing renewal rates, indicating a loyal and satisfied user base.
RECOMMENDATION PERFORMANCE METRICS
Diversity of Content Viewed​
Tracks the number of distinct genres or sub-genres viewed over a set period.​
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Balanced growth in diversity without causing drops in completion or engagement.
Indicates successful exploration. A diverse content profile shows that the system is exposing users to new and varied content.​​
Completion Rate
Percentage of recommended titles that users finish watching.
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Completion rate reflects how compelling and engaging a recommendation is. High completion rates are typically linked to effective exploitation.
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High completion rates for core genres, with gradually increasing completion for newly introduced sub-genres.
Revisit Rate
Measures how often users come back to the same genre or series over time.
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Completion rate reflects how compelling and engaging a recommendation is. High completion rates are typically linked to effective exploitation.
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Steady revisit rates with slight dips if new exploration strategies are in play.
Skip Rate
The percentage of recommended titles that users skip after watching for less than 10% of the content.​​​​
High skip rates often indicate that recommendations are missing the mark.​
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Declining skip rates over time, indicating better alignment between recommendations and user preferences.
Genre Engagement Rate​
Shows whether new genres are resonating with users or being quickly abandoned.
Measures the percentage of new genres that users continue watching beyond the first 10 minutes.​
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High engagement rate for newly introduced genres, indicating effective exploration.
RL Strategy
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Primary Strategy: EXPLORATION.
The system should regularly introduce new and diverse genres into Jason’s feed, offering content that blends different genres (e.g., Sci-Fi Comedy or Action Documentaries).
Genre Hybrids should be highlighted to keep Jason’s interest piqued.
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Impact on Personalization
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The system learns from Jason’s browsing and skip patterns to detect what types of content he enjoys most.
As Jason explores more, the RL model adjusts to present niche content that aligns with his exploratory tendencies.
RL Strategy
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Primary Strategy: EXPLOITATION.
The system should focus on Sarah’s favourite genres and suggest similar content based on her viewing patterns (e.g., Crime Dramas or True Crime Thrillers).
Adjacent Genre Exploration can be used to nudge her toward related genres (e.g., Legal Dramas or Historical Mysteries) when content fatigue is detected.
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Impact on Personalization
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Sarah’s preferences will be fine-tuned over time based on the completion rate of shows within her genre.If Sarah skips content that is slightly outside her preferred genres, the RL model will re-adjust to avoid overwhelming her with unrelated suggestions.
RL Strategy
Primary Strategy: CONTEXTUAL RECOMMENDATIONS.
The system should suggest different content based on the time of day (e.g., Family-Friendly Shows in the afternoon and Drama Thrillers in the evening).
Mood-based content adaptation will allow the system to recommend lighter content when Emily is with her kids and more serious shows when she’s alone.
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Impact on Personalization
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By tracking time-of-day patterns, the RL model can personalize Emily’s feed to match her context (e.g., suggesting more engaging, deeper content during her solo watch sessions).
The model can also learn her preference for family-oriented content in the daytime and adjust accordingly.
HOW PERSONAS HELP FINE-TUNE THE RL MODEL
1. Personalized Recommendations:
By understanding who the user is—whether they’re someone who sticks to their favorite genres or loves trying new things—the RL model can serve up content that feels just right. It knows when to give more of what they love or when to gently introduce something fresh.
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2. Constant Learning and Adapting:
The system gets smarter over time. Each interaction a user has with the platform teaches the model something new, allowing it to tweak recommendations and better match what the user wants, in real time.
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3. Keeping Users Happy and Engaged:
When the system understands a user’s preferences, it avoids suggesting content they’re likely to ignore. For example, a Genre Loyalist won’t get bombarded with random genres, while a Curious Explorer gets enough variety to stay interested. This keeps users engaged and coming back for more.
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4. Building Long-Term Relationships:
By recognizing these personas, the RL model makes more accurate, tailored recommendations that keep users hooked. Over time, this personalization strengthens the bond between the user and the platform, ensuring they stick around for the long haul.
In short, understanding these personas helps the RL model create an experience that feels personal, seamless, and ever-evolving, making sure users find what they love while keeping things fresh and engaging.
STRATEGY EVOLUTION OVER TIME
The Reinforcement Learning (RL) model isn’t just about making short-term recommendations—it learns and adapts to user behaviors over time, including seasonal patterns and gradual shifts in preferences. This long-term learning is key to keeping the user experience fresh and personalized, even as interests change throughout the year.
TESTING AND VALIDATION
After outlining the RL model and the evolution of user preferences, it’s essential to show how this system is tested and validated.
A. User Feedback Collection
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Qualitative Feedback: Conduct surveys or user interviews to gather direct insights on the recommendations. Ask users if they found the recommendations relevant, surprising, or engaging. Did they experience content fatigue or discover new content they liked?
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Quantitative Feedback: Measure success through metrics like:
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Watch time: Did users spend more time watching content after RL-driven recommendations?
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Completion rates: Did users finish more content as the model adapted to their preferences?
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Skip rates: Did skip rates decrease as the model fine-tuned recommendations?
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B. A/B Testing
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Run A/B tests comparing the RL-driven recommendation engine to traditional methods. Show if the RL approach led to higher engagement or satisfaction metrics.
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Group A: RL-driven recommendations.
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Group B: Standard genre-based recommendations.
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Analyse Results: Present the performance differences between the two groups in terms of watch time, content diversity, and satisfaction scores.