Query Understanding and Ecommerce
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Lecture presented to the eBay Search Science team on December 19, 2023.
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Query Understanding and Ecommerce
- 1. Query Understanding and Ecommerce Daniel Tunkelang
- 2. Introduction ● Many moving parts need to align to succeed in ecommerce search. ● Query understanding is perhaps the most critical of those parts. ● Query understanding offers extraordinary potential for improvement.
- 3. Overview ● Relevance and Desirability ● Guiding Searchers to Better Queries ● Search Queries vs. Search Intents ● Bags of Documents, Bags of Queries
- 5. Retrieval + ranking focus on relevance + desirability. ● Relevance = how much a result responds to the query. ● Desirability = the query-independent utility of a result. ● This is a simplified model that ignores personalization and other factors. ● But what does all this have to do with query understanding?
- 6. What matters most to the buyer is query-dependent. ● A relevance-only model would depend only on query-dependent signals. ● A smarter approach filters on relevance but then focuses on desirability. ● Queries expressing more specific intent set a higher bar for relevance. christmas ornaments gaggia brera water tank vs.
- 7. Guiding Searchers to Better Queries
- 8. Application should guide searchers to better queries. ● No query understanding model or system is ever going to be perfect. ● A search application will understand some queries but not others. ● A failure to understand the query undermines retrieval and ranking. ● Hence, autocomplete, related searches, and all other query suggestions should promote queries that the search application can understand!
- 9. Suggest unambiguous, high-specificity queries. ● A search application should never suggest queries it cannot understand. ● In particular, that means not suggesting ambiguous queries, e.g., “mixer”. ● All else equal, it should favor more specific over less specific queries. ● Queries with higher specificity tend to have higher conversion rates. ● That is why it is important to model and measure query specificity!
- 10. Search Queries vs. Search Intents
- 11. ● Information retrieval researchers worry about queries with multiple intents. jaguar or ? ● A more practical concern is multiple queries that map to the same intent. lightning to 3.5mm iphone to aux Search Query != Search Intent
- 12. Search experience should be based on the intent. Recall? CTR? Conversion Rate? ... ? or
- 13. Use queries with similar intent to increase recall. Similar but not equivalent intent.
- 14. Bags of Documents, Bags of Queries
- 15. Bag of documents: query as mean of product vectors. ► ► [0.13, 0.81, … ] [0.09, 0.75, … ] … ► [0.11, 0.79, … ] [0.13, 0.81, … ] [0.09, 0.77, … ] … ► [0.12, 0.78, … ] ► cos > 0.98 black tshirts for men mens black t-shirt
- 16. ML generalizes the bag-of-documents model to tail queries. ● Train using (query1, query2, similarity) triples from offline model. ● Oversample similar query pairs to increase sensitivity where it matters. ● Fine-tune a pre-trained micro-BERT sentence transformer model. ● Concatenate the output of a query classifier to the query keywords.
- 17. Duality: we can model a document as a bag of queries. ● A document can be modeled based on the queries intended to find it. = mens t shirt black tshirts for men … ● We can use this model to measure retrievability, which is recall in practice. ● Useful as feedback for indexing, both in general and for the particular item.
- 18. Summary ● The tradeoff between relevance and desirability depends on query specificity. ● All query suggestions should be unambiguous, preferably high-specificity. ● Measure query similarity to recognize queries with same or similar intent. ● Model queries as bags of documents, and documents as bags of queries.
- 19. One more thing…