Predicting
Missing Items in Shopping Carts
Existing research in association mining has focused mainly on
how to expedite the search for frequently co-occurring groups of items in
shopping cart type of transactions; less attention has been
paid to methods that exploit these frequent item set for prediction
purposes. This paper contributes to the latter task by proposing a technique
that uses partial information about the contents of a shopping cart for the
prediction of what else the customer is likely to buy. Using the recently
proposed data structure of item set trees
(IT-trees), we obtain, in a computationally efficient manner, all rules whose
antecedents contain at least one item from the incomplete shopping cart. Then,
we combine these rules by uncertainty processing techniques, including the
classical Bayesian decision theory and a new algorithm based on the
Dempster-Shafer (DS) theory of evidence combination.
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