There’s an interesting blog entry in the datamining blog about the extent to which free sites are motivated to offer good customer service.  The heart of the argument is that if you’re a freeloader on a site on which the bills are being paid by a small percentage of subscribers, you’re likely to see your service suffer.

The deeper argument, though, is that  sites that are sticky are likely to be able to get away with treating their freeloading customers worse over time than sites that are not sticky.  For instance, if part of the reason that you like to shop at Amazon is the recommendations they give you, you can’t switch to a different store easily, because they won’t be able to provide recommendations that are as good.  There are two reasons for this: first of all, they won’t have a personal profile for you, and second, they won’t have as much data about other customers’ behavior.  Because of this stickiness, Amazon should eventually be able to collect higher rents than other online stores.

One interesting solution to both of these problems would be portable profiles.  Consumers could demand that the businesses they buy from accept a profile in a standard format, and export useful information to that standard format.  (Check out the P3P proposal for an example of what such a profile might look like.) Then, customers could easily take their data with them to whatever business they wish to shop at.  For instance, at MovieLens we often get Netflix customers who ask us to import their Netflix profile, so they can use our recommendation engine on their Netflix data.  (We currently don’t support this, because we’re pretty sure doing so would be against Netflix’ terms of use.  We’d love for them to give us permission, though!)

There’s also an aside about the risk of news aggregators being in charge of what we see. The idea is that a news aggregator might refuse to broadly disseminate news that would oppose its interests.  This possibility returns us to an interesting recommender systems problem: how can the user of a recommendation system know that the system is making decisions that are in his or her best interests?  Is there a zero knowledge proof that might help?




Written by

Comments are closed.