If no one has bid on both words in their advertising, then being able to tell which term is the most relevant may enable a search engine to show ads that fit the most relevant term, rather than the less relevant term.
This could be done by looking at two word searches from users, and seeing if they might delete one of the words in a follow-up search. Search engineers might be able to set something up to find such deletions, and create a "deletion probability score" for terms.
A patent application from Yahoo explains how this might be done:
System and methods for ranking the relative value of terms in a multi-term search query using deletion prediction -
Search using document number 20060129534 here. (I'm having problems linking to the patent application directly, from the forums)
Here's an example of how a deletion probability score might be created.
- Look at the search engine log files for queries that use two search terms, and pull aside all of the ones that share a term like “Honda."
- The other word/term could be anything.
- See if there are any follow-up searches from the searchers who used these two term queries, and see if those subsequent searches involve deleting either “Honda” or the other term.
- If so, calculate the deletion probability score for “Honda” by:
- Count the number of times a word is deleted in a follow-up search from a user in a two word search query which includes Honda. Let's say in this instance, that happened 6059 times.
- Look at how many times Honda was the term deleted. In this example, that might have been 1874 times.
- Take the number of times Honda was deleted, divided by the number of times any word was deleted. Here, that would be 1874/6059, or about 0.31. That's the probability deletion score for Honda, for a two term query.
- That deletion probability score for Honda would then be add to the list of deletion probability scores for other terms.
It's an interesting way to track, and attempt to incorporate user behavoir to search results, and relevant ads to users.