Netflix, the popular online movie rental service, is planning to award $1 million to the first person who can improve the accuracy of movie recommendations based on personal preferences.

To win the prize, which is to be announced today, a contestant will have to devise a system that is more accurate than the company’s current recommendation system by at least 10 percent. And to improve the quality of research, Netflix is making available to the public 100 million of its customers’ movie ratings, a database the company says is the largest of its kind ever released.

Recommendation systems, also known as collaborative filtering systems, try to predict whether a customer will like a movie, book or piece of music by comparing his or her past preferences to those of other people with similar tastes. Such systems will look at, say, the last 10 books, movies or songs a customer has rated highly and try to extrapolate an 11th.

Computer scientists say that after years of steady progress in this field, there has been a slowdown — which is what Netflix executives say prompted them to offer the problem to a wide audience for solution.