In this paper, we develop and evaluate several probabilistic

models of user click-through behavior that are appropriate for

modeling the click-through rate of items that are presented

to the user in a list. Potential applications include modeling

the click-through rates of search results from a search engine,

items ranked by a recommendation system, and search advertisements

returned by a search engine. Our models capture

contextual factors related to the presentation as well as

the underlying relevance or quality of the item. We focus

on two types of contextual factors for a given item; the positional

context of the item and the quality of the other results.

We evaluate our models on a search advertising dataset from

Microsoft’s Live search engine and demonstrate that modeling

contextual factors improves the accuracy of click-through

models.

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In  AAAI

Publisher  American Association for Artificial Intelligence
All copyrights reserved by AAAI 2007.

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