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2 months ago
Leveraging Transformers to Improve Product Retrieval Results

Originally published in Amazon Science, Aug 3, 2023.

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.

In a paper we’re presenting at this year’s International Conference on Knowledge Discovery and Data Mining (KDD), we describe a new approach to learning to rank that factors in absolute feedback. It also uses the type of transformer models so popular in natural-language processing to attend to differences among items in the same list to predict their relative likelihood of being clicked.

To continue reading this article, click here.

4 thoughts on “Leveraging Transformers to Improve Product Retrieval Results

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