Ted Dunning - Practical Machine Learning

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The good news is that theres a new trend in machine learning and particularly in recommendation: very simple approaches are proving to be very effective in real-world settings. Machine learning is moving from the research arena into the pragmatic world of business. In that world, time to reflect is very expensive, and companies generally cant afford to have systems that require armies of PhDs to run them. Practical machine learning weighs the trade-offs between the most advanced and accurate modeling techniques and the costs in real-world terms: what approaches give the best results in a cost-benefit sense?

Lets focus just on recommendation. As you look around, its obvious that some very large companies have for some years put machine learning into use at large scale (see ).

Figure 1-1. What does recommendation look like?

As you order items from Amazon, a section lower on the screen suggests other items that might be of interest, whether it be OReilly books, toys, or collectible ceramics. The items suggested for you are based on items youve viewed or purchased previously. Similarly, your video-viewing choices on Netflix influence the videos suggested to you for future viewing. Even Google Maps adjusts what you see depending on what you request; for example, if you search for a tech company in a map of Silicon Valley, youll see that company and other tech companies in the area. If you search in that same area for the location of a restaurant, other restaurants are now marked in the area. (And maybe searching for a big data meetup should give you technology companies plus pizza places.)

But what does machine learning recommendation look like under the covers? shows the basics.

Figure 1-2. The math may be scary, but if approached in the right way, the concepts underlying how to build a recommender are easily understood.

If you love matrix algebra, this figure is probably a form of comfort food. If not, you may be among the majority of people looking for solutions to machine-learning problems who want something more approachable. As it turns out, there are some innovations in recommendation that make it much easier and more powerful for people at all levels of expertise.

There are a few ways to deal with the challenge of designing recommendation engines. One is to have your own team of engineers and data scientists, all highly trained in machine learning, to custom design recommenders to meet your needs. Big companies such as Google, Twitter, and Yahoo! are able to take that approach, with some very valuable results.

Other companies, typically smaller ones or startups, hope for success with products that offer drag-and-drop approaches that simply require them to supply a data source, click on an algorithm, and look for easily understandable results to pop out via nice visualization tools. There are lots of new companies trying to design such semiautomated products, and given the widespread desire for a turnkey solution, many of these new products are likely to be financially successful. But designing really effective recommendation systems requires some careful thinking, especially about the choice of data and how it is handled. This is true even if you have a fairly automated way of selecting and applying an algorithm. Getting a recommendation model to run is one thing; getting it to provide effective recommendations is quite a lot of work. Surprisingly to some, the fancy math and algorithms are only a small part of that effort. Most of the effort required to build a good recommendation system is put into getting the right data to the recommendation engine in the first place.

If you can afford it, a different way to get a recommendation system is to use the services of a high-end machine-learning consultancy. Some of these companies have the technical expertise necessary to supply stunningly fast and effective models, including recommenders. One way they achieve these results is by throwing a huge collection of algorithms at each problem, andbased on extensive experience in analyzing such situationsselecting the algorithm that gives the best outcome. SkyTree is an example of this type of company, with its growing track record of effective machine learning models built to order for each customer.

Keep it simple is becoming the mantra for successful work in the big data sphere, especially for Hadoop-based computing. Every step saved in an architectural design not only saves time (and therefore money), but it also prevents problems down the road. Extra steps leave more chances for operational errors to be introduced. In production, having fewer steps makes it easier to focus effort on steps that are essential, which helps keep big projects operating smoothly. Clean, streamlined architectural design, therefore, is a useful goal.

But choosing the right way to simplify isnt all that simpleyou need to be able to recognize when and how to simplify for best effect. A major skill in doing so is to be able to answer the question, How good is good? In other words, sometimes there is a trade-off between simple designs that produce effective results and designs with additional layers of complexity that may be more accurate on the same data. The added complexity may give a slight improvement, but in the end, is this improvement worth the extra cost? A nominally more accurate but considerably more complex system may fail so often that the net result is lower overall performance. A complex system may also be so difficult to implement that it distracts from other tasks with a higher payoff, and that is very expensive.