【正文】 der to make the output of the model even more realistic. The remainder of the paper is anized as follows. In Section 2 we will focus on the limitations of the previous PROFSET model for product selection. In Section 3, we will introduce the generalized PROFSET model. Section 4 will be devoted to the empirical implementation of the model and its results on realworld supermarket data. 外文翻譯 3 Finally, Section 5 will be reserved for conclusions and further research. 2 The PROFSET Model The key idea of the PROFSET model is that when evaluating the business value of a product, one should not only look at the individual profits generated by that product (the naive approach), but one must also take into account the profits due to crossselling effects with other products in the assortment. Therefore, to evaluate product profitability, it is essential to look at frequent sets rather than at individual product items since the former represent frequently cooccurring product binations in the market baskets of the customer. As was also stressed by Cabena et al. [5], one disadvantage of associations discovery is that there is no provision for taking into account the business value of an association. The PROFSET model was a first attempt to solve this problem. Indeed, in terms of the associations discovered, the sale of an expensive bottle of wine with oysters accounts for as much as the sale of a carton of milk with cereal. This example illustrates that, when evaluating the interestingness of associations, the microeconomic framework of the retailer should be incorporated. PROFSET was developed to maximize crossselling opportunities by evaluating the profit margin generated per frequent set of products, rather than per product. In the next Section we will discuss the limitations of the previous PROFSET model. More details can be found elsewhere [3]. Limitations The previous PROFSET model was specifically developed for market basket data from automated convenience stores. Data sets of this origin are characterized by small market baskets (size 2 or 3) because customers typically do not purchase many items during a single shopping visit. Therefore, the profit margin generated per frequent purchase bination (X) could accurately be approximated by adding the profit 外文翻譯 4 margins of the market baskets (Tj) containing the same set of items, . X = Tj. However, for supermarket data, the existing formulation of the PROFSET model poses significant problems since the size of market baskets typically exceeds the size of frequent item sets. Indeed, in supermarket data, frequent item sets mostly do not contain more than 7 different products, whereas the size of the average market basket is typically 10 to 15. As a result, the existing profit allocation heuristic cannot be used anymore since it would cause the model to heavily underestimate the profit potential from crossselling effects between products. However, getting rid of this heuristic is not trivial and it will be discussed in detail in Section . A second limitation of the existing PROFSET model relates to principles of category management. Indeed, there is an increasing trend in retailing to manage product categories as separate strategic business units [6]. In other words, because of the trend to offer more products, retailers can no longer evaluate and manage each product individually. Instead, they define product categories and define marketing actions (such as promotions or store layout) on the level of these categories