Typical components in ecommerce recommendation system include identification of primary driver items and the identification of affinity items
- Purchased shopping carts items. Items added to carts but abandoned.
- Pricing experiments online where they offer the same products at different prices and see the results.
- Wishlists – what’s on them specifically for you.
- Referral sites (identification of where you came in from can hint other items of interest)
- Dwell times (how long before you click back and pick a different item)
- Ratings by you or those in your social network/buying circles – if you rate things you like you get more of what you like and if you confirm with the “i already own it” button they create a very complete profile of you.
- Demographic information (your shipping address, etc.) – they know what is popular in your general area for your kids, yourself, your spouse, etc.
- Direct marketing click through data – did you get an email from them and click through? They know which email it was and what you clicked through on and whether you bought it as a result.
- Click paths in session – what did you view regardless of whether it went in your cart. Number of times viewed an item before final purchase
- Luckily people behave similarly in aggregate so the more they know about the buying population at large the better they know what will and won’t sell and with every transaction and every rating/wishlist add/browse they know how to more personally tailor recommendations. Keep in mind this is likely only a small sample of the full set of influences of what ends up in recommendations, etc.