Although the interest in recommender systems technology has been increasing in recent years in both industry and research, and although recommender applications can nowadays be found on many web sites of online retailers, almost no studies about the actual business value of such systems have been published that are based on real-world transaction data.

As described in Chapter 7, the performance of a recommender system is measured mainly based on its accuracy with respect to predicting whether a user will like a certain item. The implicit assumption is that the online user – after establishing trust in the system's recommendations or because of curiosity – will more often buy these recommended items from the shop.

However, a shop owner's key performance indicators related to a personalized web application such as a recommender system are different ones. Establishing a trustful customer relationship, providing extra service to customers by proposing interesting items, maintaining good recommendation accuracy, and so on are only a means to an end. Although these aspects are undoubtedly important for the long-term success of a business, for an online retailer, the important performance indicators are related to (a) the increase of the conversion rate – that is, how web site visitors can be turned into buyers, and (b) questions of how to influence the visitors in a way that they buy more or more profitable items.

Unfortunately, only few real-world studies in that context are available because large online retailers do not publish their evaluations of the business value of recommender systems.