Practical Machine Learning: Innovations in Recommendation

Chapter 1. Practical Machine Learning

A key to one of most sophisticated and effective approaches in machine learning and recommendation is contained in the observation: “I want a pony.” As it turns out, building a simple but powerful recommender is much easier than most people think, and wanting a pony is part of the key.

Machine learning, especially at the scale of huge datasets, can be a daunting task. There is a dizzying array of algorithms from which to choose, and just making the choice between them presupposes that you have sufficiently advanced mathematical background to understand the alternatives and make a rational choice. The options are also changing, evolving constantly as a result of the work of some very bright, very dedicated researchers who are continually refining existing algorithms and coming up with new ones.

What’s a Person To Do?

The good news is that there’s 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 can’t 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?

Let’s focus just on recommendation. ...