TAILIEUCHUNG - Sequential Event Prediction with Association Rules

In this section, we articulate the roadblocks that must be addressed to make a market/systems integration suc- cessful. In our opinion, these challenges are not in the market details. Rather, we think that the biggest chal- lenges to their adoption in systems will come fromunder- standing, supporting, and using these mechanisms. After presenting each challenge, we consider action items for the general systems community, as well as for systems market designers where appropriate. In our view, a mar- kets/systems integration could fail if these challenges are not overcome: Allocation Policy Must be Explicit. One of the un- comfortable realities of a market is that it forces user communities to confront their social allocation. | Sequential Event Prediction with Association Rules Cynthia Rudin MIT rudin@ Benjamin Letham MIT bletham@ Ansaf Salleb-Aouissi Columbia University ansaf@ Eugene Kogan Sourcetone David Madigan Columbia University madigan@ Abstract We consider a supervised learning problem in which data are revealed sequentially and the goal is to determine what will next be revealed. In the context of this problem algorithms based on association rules have a distinct advantage over classical statistical and machine learning methods however there has not previously been a theoretical foundation established for using association rules in supervised learning. We present two simple algorithms that incorporate association rules and provide generalization guarantees on these algorithms based on algorithmic stability analysis from statistical learning theory. We include a discussion of the strict minimum support threshold often used in association rule mining and introduce an adjusted confidence measure that provides a weaker minimum support condition that has advantages over the strict minimum support. The paper brings together ideas from statistical learning theory association rule mining and Bayesian analysis. 1 Introduction Given a sequence database of past event sequences to learn from we aim to predict the next event within a current event sequence. Consider for instance the data generated by a customer placing items into the virtual basket of an online grocery store such as NYC s Fresh Direct Peapod by Stop Shop or Roche Bros. The customer adds items one by one into the current basket creating a sequence of events. The customer has identified him- or herself so that all past orders are known. After each item selection a confirmation screen contains a small list of recommendations for items that are not already in the basket. If the store can find patterns within the customer s past purchases it may be able to .

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