The methods we have looked at so far work well when you have numeric ratings of how much a user liked a product. This type of information is not always available, as it requires active behavior on the part of consumers.
Basket analysis is an alternative mode of learning recommendations. In this mode, our data consists only of which items were bought together; it does not contain any information on whether or not individual items were enjoyed. Even if users sometimes buy items they regret, on average, knowing their purchases gives you enough information to build good recommendations. It is often easier to get this data rather than rating data, as many users will not provide ratings, while the basket data is generated as a side effect of shopping. The following screenshot shows you a snippet of Amazon.com's web page for Tolstoy's classic book War and Peace...
