4. The method of claim 1 , wherein weighting the vectors comprises weighting the features of the received query with w(f, Q)=(1+log #(f, Q))·idf(f), where #(f, Q) comprises a number of times feature f occurs in query Q and idf(f) comprises an inverse document frequency for feature f in an ad corpus, further comprising: weighting the features of the expanded queries (Q′) stored in the query feature index with w ⁡ ( f , E ⁡ ( Q ′ ) ) = ( 1 + log ⁢ ∑ D ∈ Results ⁡ ( Q ′ ) ⁢ # ⁢ ( f , D ) ) · idf ⁡ ( f ) , where E(Q′) comprises the search results-expanded version of query Q′, Results(Q′) comprises a set of top search results for query Q′, and #(f, D) comprises a number of times that feature f occurs in search result D.
4. The method of claim 1 , wherein weighting the vectors comprises weighting the features of the received query with w(f, Q)=(1+log #(f, Q))·idf(f), where #(f, Q) comprises a number of times feature f occurs in query Q and idf(f) comprises an inverse document frequency for feature f in an ad corpus, further comprising: weighting the features of the expanded queries (Q′) stored in the query feature index with w ⁡ ( f , E ⁡ ( Q ′ ) ) = ( 1 + log ⁢ ∑ D ∈ Results ⁡ ( Q ′ ) ⁢ # ⁢ ( f , D ) ) · idf ⁡ ( f ) , where E(Q′) comprises the search results-expanded version of query Q′, Results(Q′) comprises a set of top search results for query Q′, and #(f, D) comprises a number of times that feature f occurs in search result D. 5. The method of claim 4 , further comprising: normalizing, under an I 2 norm, the vectors of the received and expanded queries to a predetermined length within each feature type.