Search-Based Methods for Recommendation Systems

The article from Amazon indsiders has been so helpful in my search for explanations of how different recommendation systems work. It's chock-full of information, so I've been breaking it down into little bits, in hopes that I fully understand it and that it makes at least some sense to those of you reading.

Search-based methods look at what a user has shown preference to, either through purchase, rating or other habits unique to individual sites, for instance listening on Pandora or Last.fm. It takes those preferences, looks at their traits and then finds other items that have the same or similar traits.

The issue here is that users most likely will not "discover new, relevant and interesting items," because their recommendations are based only on what they already like. The article explains this well: if someone bought The Godfather, then their results could range from best-selling drama DVDs or movies all from Francis Ford Coppola. There's either too much of a gap between what's liked and what's recommended, or too little.

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