Assessing the absolute utility of query results, rather than just their relative utility, improves learning-to-rank models.
When a customer clicks on an item in a list of product-search results, it implies that that item is better than those not clicked. “Learning to rank” models leverage such implicit feedback to improve search results, comparing clicked and unclicked results in either “pairwise” (comparing pairs of results) or listwise (judging a results position within the list) fashion.
A problem with this approach is the lack of absolute feedback. For instance, if no items in the selection are clicked, it’s a signal that none of the results was useful. But without clicked items for comparison, learning-to-rank models can do nothing with that information. Similarly, if a customer clicks on all the items in a list, it could indicate that all the results were useful — but it could also indicate a fruitless search to find even one useful result. Again, learning-to-rank models can’t tell the difference.
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