Predicting preference flips in commerce search

Samuel Ieong, Nina Mishra, Or Sheffet

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations


Traditional approaches to ranking in web search follow the paradigm of rank-by-score: a learned function gives each query-URL combination an absolute score and URLs are ranked according to this score. This paradigm ensures that if the score of one URL is better than another then one will always be ranked higher than the other. Scoring contradicts prior work in behavioral economics that preference between items depends not only on the items but also on the presented alternatives. Thus, for the same query, preference between items A and B may depend on the presence or absence of item C. We propose a new model of ranking, the Random Shopper Model, that allows and explains such behavior. In this model, each feature is viewed as a Markov chain over the items to be ranked, and the goal is to find a weighting of the features that best reflects their importance. We show that our model can be learned under the empirical risk minimization framework, and give an efficient learning algorithm. Experiments on commerce search logs demonstrate that our algorithm outperforms scoring-based approaches including regression and listwise ranking.

Original languageEnglish
Title of host publicationProceedings of the 29th International Conference on Machine Learning, ICML 2012
Number of pages8
StatePublished - 2012
Externally publishedYes
Event29th International Conference on Machine Learning, ICML 2012 - Edinburgh, United Kingdom
Duration: 26 Jun 20121 Jul 2012

Publication series

NameProceedings of the 29th International Conference on Machine Learning, ICML 2012


Conference29th International Conference on Machine Learning, ICML 2012
Country/TerritoryUnited Kingdom


Dive into the research topics of 'Predicting preference flips in commerce search'. Together they form a unique fingerprint.

Cite this