
Rethinking Recommendation Engines - jmorin007
http://www.readwriteweb.com/archives/rethinking_recommendation_engines.php
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ChaitanyaSai
This is what I came away with after working on the Netflix dataset.

Incentives, not perceptions, are what need to be changed for realizing better
recommendation engines (or collaborative filtering algorithms; whatever you
call them). Negative recommendations are not a good idea. Unlike false
positives, the consumer really has no way of knowing whether a negative was in
reality true or false. Recommendations are also about feeding our ego , even
if the engine is ultimately basing its suggestions on consumers whose tastes
are very similar to yours. It is indeed magical; consumers who think they are
expanding horizons by seeking out indies or documentaries on Netflix are
simply discovering latent interests that have already been explored by others
in your interest cluster. The author is right though; false positives are what
weigh most heavily on our perceptive scales. Risk aversion ensures that people
do not venture out beyond what are suggested to them (by an automated agent or
friend), and collaborative filtering algorithms have a cold start problem. For
a movie to appear on the radar, enough people need to have watched and
expressed enjoyment. Digital filtering makes it even harder for random
exploration that is needed to seed these. Failing that, you get middling
recommendations like that of Netflix, where the average rating (~=3.6) is no
better that what you would get without it. There is one easy way to get around
the risk aversion obstacle. Offer "free" movies from a different, high
variance list of movies, with "free" meaning a fourth movie on a three-per-
month subscription, and variance being high on the predicted ratings of the
consumer. Such an offer, while being marginally more expensive for Netflix,
will allow consumers to experiment without any perceived costs (which,
currently is the wait for the next Wire or Deadwood DVD you could have gotten
otherwise). If I may speculate, recommendations should be about gentle, guided
exploration and not avoidance.

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dejb
I wonder if an expert (human) with access to the same data set, any automated
tools they choose and their own knowledge/research of movies could do a lot
better. I think they probably would be able to crack the 10%. If they couldn't
then it would be a fair indication that the data is simply too noisy to beat
the 10%.

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Spyckie
Interesting... even though accuracy is generally high, the false positives
weigh heavily on the experience.

How accurate can we get recommendation systems anyways? I know my friends
recommend movies to me all the time and a lot of them aren't my taste - do we
expect computers to outperform our close acquaintances?

