Predicting missing items in shopping carts

Kasun Wickramaratna, Miroslav Kubat, Kamal Premaratne

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

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 itemsets" 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 itemset 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.

Original languageEnglish (US)
Article number4693707
Pages (from-to)985-998
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume21
Issue number7
DOIs
StatePublished - Jul 1 2009

Keywords

  • Dempster-Shafer theory
  • Frequent itemsets
  • Uncertainty processing

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Information Systems
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Predicting missing items in shopping carts'. Together they form a unique fingerprint.

Cite this