Your Story on the Sofa with the Remote
On a quiet evening, you lean comfortably on the sofa and browse the streaming service’s home screen. Posters of countless movies and dramas flash before your eyes. Suddenly, one drama catches your attention — a new release with a similar vibe to the series you binge-watched just yesterday. “Huh? How did it know?”
Astonishing recommendations that seem to read your mind — aren’t you curious about the secret? Today, we’ll meet the star behind this secret, a somewhat unfamiliar friend called the ‘real-time user behavior analysis algorithm.’ Sounds complicated? Don’t worry. From now on, through a story, I’ll explain how this clever friend makes us happy in an easy and fun way.
First Clue: Every Action You Take Becomes a ‘Signal’
The story begins the moment you grab the remote. The speed at which you scroll through content, the moments you pause in front of certain posters, the act of playing trailers — each of these is a valuable ‘signal’ to the algorithm.
Our protagonist, the ‘algorithm,’ is a detective collecting these signals in real time.
- Clicks and plays: “Ah, this user is interested in this genre!”
- Viewing time: “They stopped watching this drama after 10 minutes. Must have been boring.”
- ‘Likes’ and ‘Favorites’: “They really liked this. Let’s find more similar content!”
- Rewinds and replays: “Watching this scene repeatedly means they must like this actor or a particular direction.”
- Search keywords: “Recently searched for words like ‘space’ or ‘mystery.’ Need to show related content.”
This clever detective’s first mission is to meticulously gather every tiny piece of your behavior data without missing a thing.
Second Clue: Finding People Like Me, ‘Collaborative Filtering’
Now, the algorithm detective’s notebook is full of clues about your tastes. But that’s not enough. To find hidden gems you haven’t discovered yet, it needs to look beyond.
Enter the magic of ‘Collaborative Filtering.’ The name sounds complex, but the principle is simple — just like the saying “birds of a feather flock together.”
The algorithm finds other users who like similar content and consume it in similar patterns. It’s like finding a best friend who shares your movie tastes.
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“User A, who has a taste similar to yours, recently enjoyed this movie. You might like it too!”
This method recommends content liked by people in your ‘taste community.’ The thrilling moment when you discover new content you didn’t know but end up loving is thanks to collaborative filtering. If you’ve ever seen a phrase like “Content watched by members with tastes similar to OO” on Netflix, you’ve experienced this magic.
Third Clue: Uncovering the Secrets of the Content Itself, ‘Content-Based Filtering’
But what if you have very unique tastes or are a new user just starting the service? Without comparable users, recommendations become difficult.
Don’t worry. Our detective has another secret weapon: ‘Content-Based Filtering.’ Instead of referencing other users, this method dives deep into the characteristics of the content itself.
The algorithm tags every piece of content with invisible labels.
- Movies/Dramas: Genre (romance, thriller), director, actors, country of production, time setting, story keywords (revenge, growth, time travel), etc.
- Music: Genre (jazz, rock), artist, album, mood (energetic, calm), instruments used, etc.
If you enjoy movies featuring a certain actor or music from the 80s, the algorithm remembers those ‘tags’ and brings other content with similar tags to you.
“The ‘Space Adventure’ you enjoyed has tags #SF #SpaceOpera #Aliens. Here are other movies with the same tags!”
This approach digs deeply into your tastes, offering delicate recommendations that add depth rather than breadth to your preferences.
The Harmony of Two Magic Spells for More Perfect Recommendations
In fact, most services use a ‘Hybrid Model’ that blends these two methods: collaborative filtering and content-based filtering.
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They consider the choices of people like you (collaborative filtering) while also taking into account the unique features of content you liked (content-based filtering). Additional information such as the time you use the service (long movies on weekend evenings, short clips during commutes) and the device you use (TV, smartphone) further refines the recommendations.
Ultimately, the magical recommendations unfolding before your sofa are thanks to the brilliant work of a smart algorithm detective who listens to all your actions, finds friends like you, and sees through the heart of the content.
Next time you open a streaming service, don’t just pass by the recommended list on the home screen. It contains the algorithm’s intense effort and care to win your heart. What kind of recommendation would you like to receive?