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I'm cynical about how machine learning of this type might be used in practice and this is an illustration of why: the stated goal is a "you might also like" sec
by ronaldx 13y ago
I'm cynical about how machine learning of this type might be used in practice and this is an illustration of why: the stated goal is a "you might also like" section.
There is no reason to believe the results are any better than a random method in respect of the goal (and it's reasonable to believe they may be worse) - we would have to measure this separately by clickthrough rate or user satisfaction survey, perhaps.
I believe you would get far better results by always posting the three most popular articles. If you want to personalise, post personally-unread articles. A lot less technical work, a lot less on-the-fly calculation, a lot more effective. The machine learning tools do not fit the goal.
The most effective real example of a "you might also like" section is the Mail Online's Sidebar of Shame. As best as I can tell, they display their popular articles in a fixed order.
Machine Learning seems to make it easy to answer the wrong question.
- andreasvc 13y agoThis is really only an argument that the example in the article is not realistic (which it doesn't have to be, it might be expository). There are in fact countless applications of machine learning in actual daily use, such as detecting credit card fraud, where simpler manual methods would perform measurably worse in terms of money lost.
- ronaldx 13y agoSure, there are realistic applications of Machine Learning with great results. But the article has failed in its headline goal ("ML is easier than it looks") if it chooses an example that is mathematically more complicated and still less effective than a naive alternative. The first difficult task is to identify an effective method.