Understanding Recommendation Systems: Increases Sales by 10% – 30%

Source: IEEE Spectrum, Oct 2012

Driven by computer algorithms, recommenders help consumers by selecting products they will probably like and might buy based on their browsing, searches, purchases, and preferences. Designed to help retailers boost sales, recommenders are a huge and growing business. Meanwhile, the field of recommender system development has grown from a couple of dozen researchers in the mid-1990s to hundreds of researchers today—working for universities, the large online retailers, and dozens of other companies whose sole focus is on these types of systems.

Over the years, recommenders have evolved considerably. They started as relatively crude and often inaccurate predictors of behavior. But the systems improved quickly as more and different types of data about website users became available and they were able to apply innovative algorithms to that data. Today, recommenders are extremely sophisticated and specialized systems that often seem to know you better than you know yourself. And they’re expanding beyond retail sites. Universities use them to steer students to courses. Cellphone companies rely on them to predict which users are in danger of switching to another provider. And conference organizers have tested them for assigning papers to peer reviewers.

Over the years, the developers of recommender systems have tried a variety of approaches to gather and parse all that data. These days, they’ve mostly settled on what is called the personalized collaborative recommender. That type of recommender is at the heart of Amazon, Netflix, Facebook’s friend suggestions, and Last.fm, a popular music website based in the United Kingdom. They’re “personalized” because they track each user’s behavior—pages viewed, purchases, and ratings—to come up with recommendations; they aren’t bringing up canned sets of suggestions. And they’re “collaborative” because they treat two items as being related based on the fact that lots of other customers have purchased or stated a preference for those items, rather than by analyzing sets of product features or keywords.

most recommenders today rely on an “item-item” algorithm, which calculates the distance between each pair of books or movies or what have you according to how closely users who have rated them agree. People who like books by Tom Clancy are likely to rate books by Clive Cussler highly, so books by Clancy and Cussler are in the same neighborhood. Distances between pairs of items, which may be based on the ratings of thousands or millions of users, tend to be relatively stable over time, so recommenders can precompute distances and generate recommendations more quickly. Both Amazon and Netflix have said publicly that they use variants of an item-item algorithm, though they keep the details secret.

Recommenders have two other features that dramatically affect the recommendations you see: First, beyond figuring out how similar you are to other shoppers, the recommender has to figure out what you actually like. Second, the system operates according to a set of business rules that help ensure its recommendations are both helpful to you and profitable for the retailer.

To build trust, the more sophisticated recommender systems strive for some degree of transparency by giving customers an idea of why a particular item was recommended and letting them correct their profiles if they don’t like the recommendations they’re getting.

… analyst Jack Aaronson of the Aaronson Group estimates that investments in recommenders bring in returns of 10 to 30 percent, thanks to the increased sales they drive. And they still have a long way to go.

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