![]() An email recipient recommendation algorithm can be a very useful tool an algorithm for this purpose was recently implemented on a very large scale by Google and is integrated into the Gmail system used by millions of people (Roth et al. Given a partial list of recipients on an email, we wish to predict the remaining recipients. The same formulation used for the online grocery store recommender system can be directly applied to email recipient recommendation. Our formulation allows for models of user behavior to be incorporated while we learn the recommender system. In particular, the customer may add items in the order provided by the recommender system, which means the predictions actually alter the sequence in which events appear. The customer may not have an explicit preference for the order in which items are added, rather he or she may add items in whichever order is most convenient. For an online grocery store recommender system, items are added to the basket one at a time. For instance, there has recently been work showing that measurements of user behavior can be used to improve search engine rankings (Agichtein et al. Recommender systems are a particularly interesting example of sequential event prediction because the predictions are expected to influence the sequence (e.g., Senecal and Nantel, 2004), and any realistic algorithm should take this into account. We apply our algorithms to data from three applications: an online grocery store recommender system, email recipient recommendation, and medical condition prediction. These algorithms are based on the principle of empirical risk minimization (ERM). In this work, we present optimization-based algorithms for sequential event prediction. ( 2011, 2012), who created a theoretical foundation along with some simple algorithms based on association rules. Motivated by this application, “sequential event prediction” was formalized by Rudin et al. The recommendations are updated as items are added to the basket. Online grocery stores such as Fresh Direct (in NYC) use the customer’s current shopping cart to recommend other items. The playlist is modified as new preferences are revealed. Pandora, use a set of songs for which the user has revealed his or her preference to construct a suitable playlist. Medical conditions occur over a timeline, and the conditions that the patient has experienced in the past can be used to predict conditions that will come (McCormick et al. There are many examples of sequential prediction problems. Predictions for the next event are updated each time a new event is revealed. We have access to a “sequence database” of past event sequences that we can use to design the predictions. The goal is to use the set of revealed events, but not necessarily their order, to predict the remaining (hidden) events in the sequence. Sequential event prediction refers to a wide class of problems in which a set of initially hidden events are sequentially revealed. We apply our approach to an online grocery store recommender system, email recipient recommendation, and a novel application in the health event prediction domain. This leads to sequential event prediction algorithms involving a non-convex optimization problem. In recommender system applications, the observed sequence of events depends on user choices, which may be influenced by the recommendations, which are themselves tailored to the user’s choices. ![]() We show how specific choices within this approach lead to different sequential event prediction problems and algorithms. Our formalization of sequential event prediction draws on ideas from supervised ranking. Such applications arise in recommender systems, equipment maintenance, medical informatics, and in other domains. We focus on applications where the set of the past events has predictive power and not the specific order of those past events. In sequential event prediction, we are given a “sequence database” of past event sequences to learn from, and we aim to predict the next event within a current event sequence. ![]()
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