Why is the Netflix Prize so challenging? [closed]

Question

Having just read the recent article in Wired, I'm curious: what is it about the Netflix Prize that's so challenging? I mean this in the sincerest way possible, I'm just curious about the difficulties posed by the contest. Are most recommendation engines in general this hard to improve? If so, why is that? Or, is Netflix unusually difficult to improve, and if this is the case, what's special about Netflix that makes this so much more challenging than, say, Amazon?

Answer 1

Recommender systems suffer from problems that are hard to fix:

• Cold start - In a new system or with a new user, there isn't enough data to create an accurate statistical model for a recommendation.
• Rating bias - If you base recommendations on user ratings, users that rate often sway the results toward their taste. If you're the type of person that doesn't like the extra step of rating, it's possible people with similar taste don't like rating either so their opinions are excluded from recommendations.
• Items that are not rated are less likely to be rated - if you select, and therefore rate, items based on their ratings, items that aren't rated are less visible and will have a hard time getting the ratings they need to affect recommendations. In the other direction, popular items have more visibility, are rated more often, and therefore play a larger part in recommendations.
• Temporal bias - Users' ratings change with time. With long-term changes, you can compensate by adding a time element to your recommendations. Short-term changes are harder to fix. After a Chuck Norris marathon, you may be more likely to give action movies high marks. The next day, after crying your eyes out to Steel Magnolias, you may be temporarily biased against action movies.
• Varying motives - in item-based recommender systems, the knitting book you purchased for your aunt's birthday will skew your recommendations (if you don't take the time to tell the system not to use it). You may give a bad kids' movie a high rating because your kids loved it.

All together, this makes recommender systems hard to improve past just-okay. A system with 80% accuracy seems great but is wrong 1 out of 5 times. This makes them more trouble than they're worth for some users.