Netflix is offerring $1 million to anyone who can improve their system for making recommendations by 10% or more. Why is this human factors? Because Netflix's business model is based on getting people to rent many movies. If the customer already knows what movies they want, this is easy. But what happens when he/she runs out of ideas? If they stop renting, Netflix loses. So they need a way to get people to rent movies they didn't know about. Random recommendations would never work, so they need some way to predict their customers' schemas for "good movie." But everyone has a different set of attributes for this. So they need to be able to model their customers' individual schemas of "good movie" based on the limited information they collect about each one, which includes:
1. some basic demographics they collect during registration
2. a list of the movies they have renting so far and when (more recent is more relevant)
3. a list of movies they have browsed but not rented
4. lists of favorite movies or wish lists
5. ratings of movies they have seen in the past
Then they develop algorithms that can include:
1. Movies that people who liked the same movies also liked
2. Movies that people with similar demographics also liked
3. Movies that share attributes with movies that this customer liked.
If you are interested in trying to win the $1 million, let me know. I would be happy to share with you the research I have done in this area.
