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IMDB ratings list extraction tool for mapping preferences and recommendations

Miloš Stanković
Miloš Stanković Apr 28, 2022
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A browser extension that would extract the ratings you gave to movies on IMDB and map out your preferences in several categories. Making a hierarchy network of preferences and then recommendations.
  • Selecting which movie to watch next on streaming services has become possibly even more time consuming than channel surfing was. Netflix, Google, and IMDB algorithms for recommending movies are not useful for the most part and are not transparent. Movie tickets are quite expensive.
  • A breakdown of your own past preferences would work better to steer you towards stories you're likelier to enjoy. Leading to spending time and money better.
  • Results can be a topic of conversation for cinephiles.
  • For IMDB or a third-party extension creator, selling a better packaged user data deal to movie studios for market research.
How it works:
People have been using IMDB for years and have recorded a lot of quality data points in it. I'm at nearly 700 ratings.
A browser extension takes a user's list and records the good ratings (7, 8, 9, 10) the user gave to feature films, then it looks at the directors, lead actors, actresses, maybe supporting cast, possibly cinematographers. It makes a map for each of these categories, illustrating just how many films with their involvement the user enjoyed.
On the vertical axis, you would have values for how many films rated 7 or higher a person has been connected with. On the horizontal axis, you have the total rating of those films. Basically, like the header image but with far more inclusions, interactivity, and a numbered grid.
When you hover over the position you'd get a pop up of movies you rated highly from the actor and their star ratings listed compactly.
This mapping is done for all possible categories. All for your viewing pleasure and to determine with better certainty what and whose work you like the most. You can send the map to friends, and even intertwine your maps for comparison too.
When it's in the basic movie category, the extension qualifies and maps the movies you liked based on genre, runtime, release year, and language. Where the rating is on one axis, and one of these values is on the other.
The other side of the app is the recommendation aspect.
It ranks unwatched movies based on pre-selected categories. Combination of a director whose three movies you liked with a 9 or a 10 rating, with an actor whose five movies you liked with an 8 rating would be a top movie recommendation for instance. You can filter based on actress, genre, director, and release years.
You get an alert for when a movie with your most-liked moviemakers is about to be released.
Additional metrics:
In list mode, you can go through the movies and select which ones have you seen in the theatre. The rest were at home viewings.
For the new movies, you rate you will automatically select whether you watched them in the movie theatre. Also, did you view the movie alone, or in company. Both are important metrics for finding a pattern of what works for you best and also get mapped out.
The extension can be used by the movie industry to graphically present which demographic prefers what by aggregating the data. It also then serves for precise marketing.
The extension is completely free if you allow for the app to sell your data to movie studios. You have an option to pay for the use and then you don't give out your data.
IMDB is a massive website, 59th in the world, so the market is there. I also feel that this mapping is something people would share on social media.
Creative contributions

Deviation from the average user score list

Miloš Stanković
Miloš Stanković Jul 10, 2022
Having the tool extract your votes and align them with the average vote rating on the movie would also be cool and something that a lot of people would share. It's mapping your own taste to the ones of your fellow movie enthusiasts.
Would be cool to see how many times you are aligned with the masses, and how often you rate higher or lower. And to measure up with your friends, have their scores as a third or fourth column in comparison.
The purpose is that if you rate sci-fi movies higher than the majority in 90% of cases, then you can start giving your time to more poorly rated sci-fi movies you still haven't watched. If you rate horror movies lower than the majority, maybe start skipping them unless they are most intriguing to you.
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General comments

Miloš Stanković
Miloš Stanković2 years ago
Afterwards, I realized this could be viral marketing just like Spotify's Wrapped action where they presented the users with a breakdown of their habits in the past year of listening to music and podcasts on their platform. Apparently, it boosted the app downloads by 21% in one week.
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