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Click models are an important tool for leveraging user feedback, and are usedby commercial search engines for surfacing relevant search results. However,existing click models are lacking in two aspects. First, they do not shareinformation across search results when computing attractiveness. Second, theyassume that users interact with the search results sequentially. Based on ouranalysis of the click logs of a commercial search engine, we observe that thesequential scan assumption does not always hold, especially for sponsoredsearch results. To overcome the above two limitations, we propose a new clickmodel. Our key insight is that sharing information across search results helpsin identifying important words or key-phrases which can then be used toaccurately compute attractiveness of a search result. Furthermore, we arguethat the click probability of a position as well as its attractiveness changesduring a user session and depends on the user's past click experience. Ourmodel seamlessly incorporates the effect of externalities (quality of othersearch results displayed in response to a user query), user fatigue, as well aspre and post-click relevance of a sponsored search result. We propose anefficient one-pass inference scheme and empirically evaluate the performance ofour model via extensive experiments using the click logs of a large commercialsearch engine.

Modeling Attractiveness and Multiple Clicks in Sponsored Search Results