| 摘要 |
A model based on a generalization of the Examination Hypothesis is disclosed that states that for a given query, the user click probability on a document in a given position is proportional to the relevance of the document and a query specific position bias. Based on this model the relevance and position bias parameters are learned for different queries and documents. This is done by translating the model into a system of linear equations that can be solved to obtain the best fit relevance and position bias values. A cumulative analysis of the position bias curves may be performed for different queries to understand the nature of these curves for navigational and informational queries. In particular, the position bias parameter values may be computed for a large number of queries. Such an exercise reveals whether the query is informational or navigational. A method is also proposed to solve the problem of dealing with sparse click data by inferring the goodness of unclicked documents for a given query from the clicks associated with similar queries.
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