User Intent Based Online Advertising. ADKDD 2010. Motivation. From the publisher/end user point of view: Most traditional approach for online advertising are content based, and those methods mainly deliver ads according to domains
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3. Replace all entities in , and get a pattern list PAT
5. Build a bipartite graph V=(PAT,URL), if there are k clicks between pattern pat and URL u, the weight for edge (pat,u) is k, note if query q can be represented by pattern pat, and q clicked u, we say pat click u
6. Manually select
7. Starting from , we use random walk to expend the seeds patterns, and get the final training data
, where represents the weight for edge (j,k)
we filter out URL u, having entropy(u) > 0