Just how accurate are advertising predictions produced by machine learning systems?
Thanks to Mikio Braun, who on Thursday, January 2015 published an article on the InfoQ website. Braun’s article includes mention of a Google acknowledgement about the role played by machine learning (also known, at least in part, as data processing by algorithms) as a predictive tool in its ad placement technology for its click ad business. Readers interested in this topic should read Braun’s article, which is titled Google on the Technical Debt of Machine Learning.
I have written about the inaccuracy of click advertising in earlier posts to this blog. To quickly summarize my opinion on this topic, I found the systemic tendency towards poor ad placement to be especially difficult to overcome when the items to be promoted provide subjective, intangible benefit. So gaining a perspective on just how much of the ad placement technology behind Google Adwords and, in all likelihood, its direct competitors (principally Microsoft’s Bing advertising system), as Braun points out in this short article is very helpful.
What is also very helpful in Braun’s article is the manner in which the Google researchers (Braun’s article is really a news report on a presentation at a recent conference event held in Montreal, the Software Engineering for Machine Learning workshop, part of the annual Neural Information Processing Systems, NIPS, conference held in Montreal) shed light on the precariousness of proper performance for machine learning systems, in this (online advertising) context, given the effect they have on other related computer processes. These researchers make clear how the basic assumptions powering Neural Networks can actually adversely affect these siblings, and, thereby, produce erroneous results along with very little value to people depending on them. Readers should note this conclusion is my own, and not a conclusion expressed anywhere in Braun’s article.
From Braun’s article, and the technical précis of a research paper on the algorithmic process behind machine learning, which was also published by Google researchers, online advertisers should be careful to set realistic expectations about paid placements. Perhaps it will make sense to horizontally structure these campaigns, with a panoramic reach wherever possible, if they are to produce any meaningful results.
Ira Michael Blonder
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