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    <p><strong>Simple and straightforward (order cart):</strong></p> <p>Keep a list of transactions in terms of what items were ordered together. For instance when someone buys a camcorder on Amazon, they also buy media for recording at the same time.</p> <p>When deciding what is "suggested" on a given product page, look at all the orders where that product was ordered, count all the other items purchased at the same time, and then display the top 5 items that were most frequently purchased at the same time.</p> <p>You can expand it from there based not only on orders, but what people searched for in sequence on the website, etc.</p> <p><strong>In terms of a rating system (ie, movie ratings):</strong></p> <p>It becomes more difficult when you throw in ratings. Rather than a discrete basket of items one has purchased, you have a customer history of item ratings. </p> <p>At that point you're looking at data mining, and the complexity is tremendous.</p> <p>A simple algorithm, though, isn't far from the above, but it takes a different form. Take the customer's highest rated items, and the lowest rated items, and find other customers with similar highest rated and lowest rated lists. You want to match them with others that have similar extreme likes and dislikes - if you focus on likes only, then when you suggest something they hate, you'll have given them a bad experience. In suggestions systems you always want to err on the side of "lukewarm" experience rather than "hate" because one bad experience will sour them from using the suggestions.</p> <p>Suggest items in other's highest lists to the customer.</p>
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    1. POAlgorithm for suggesting products
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    1. COYou can then further that along by only checking for product searches done by people of the same demographic information (caucasian males aged 18-24 for example).
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    2. COIdeally you'll segment the market very finely, demographics, tagging, review keywords, etc will all feed into your suggestion engine. It can become very, very complex very quickly... But for a simple first order system the above can work very well. Once it's in place, you can tweak the algorithm and data sources to your heart's content...
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