This paper presents a novel intelligent bidding system, called SOABER (Simultaneous Online Auction Bidder), which monitors simultaneous online auctions of high-value fine art items. It supports decision making by maximizing bidders’ surplus and their chances of winning an auction. One key...

The Expectation-Maximization (EM) algorithm is a very popular optimization tool for mixture problems and in particular for model-based clustering problems. However, while the algorithm is convenient to implement and numerically very stable, it only produces local solutions. Thus, it may not...

Creating a loyal customer base is one of the most important, and at the same time, most difficult tasks a company faces. Creating loyalty online (e-loyalty) is especially difficult since customers can "switch" to a competitor with the click of a mouse. In this paper we investigate e-loyalty in...

Pre-release demand forecasting is crucial for allocating limited marketing resources and is one of the most challenging tasks facing decision makers. In this study, we present a powerful pre-release forecasting method via functional shape analysis (FSA) of the trading price histories of an...

The flexibility of time and location as well as the availability of an abundance of both old and new products makes online auctions an important part of people's daily shopping experience. While many bidders rely on variants of the well-documented early or last-minutes bidding strategies,...

Even though auctions are capturing an increasing share of commerce, they are typically treated in the theoretical economics literature as isolated. That is, an auction is typically treated as a single seller facing multiple buyers or as a single buyer facing multiple sellers. In this paper, we...

The Expectation-Maximization (EM) algorithm is a very popular optimization tool for mixture problems and in particular for model-based clustering problems. However, while the algorithm is convenient to implement and numerically very stable, it only produces local solutions. Thus, it may not...