Predicting missing items in shopping carts
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09-04-2010, 08:41 PM

Existing research in association mining has focused mainly on the search for frequently co-occurring groups of items in shopping cart type of transactions. This project and implimentation proposes 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

THE primary task of association mining is to detect frequently co-occurring groups of items in transactional databases. The intention is to use this knowledge for prediction purposes: if bread, butter, and milk often appear in the same transactions, then the presence of butter and milk in a shopping cart suggests that the customer may also buy bread. More generally, knowing which items a shopping cart contains, we want to predict other items that the customer is likely to add before proceeding to the checkout counter.

Base Paper Presented By:
Kasun Wickramaratna, Student Member, IEEE, Miroslav Kubat, Senior Member, IEEE, and
Kamal Premaratne, Senior Member, IEEE

Key technologies Used in this project and implimentation: java
Product/ Platform used in this project and implimentation: Microsoft

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09-11-2012, 10:49 AM

hi you can refer these pages to get the details on predicting missing items in the shopping cart


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30-09-2013, 11:29 AM

i need predicting missing items in shopping carts coding in

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