3 papers
cs.IR2019
Ranking sentences from product description & bullets for better search
Prateek Verma, Aliasgar Kutiyanawala, Ke Shen
Products in an ecommerce catalog contain information-rich fields like description and bullets that can be useful to extract entities (attributes) using NER based systems. However,…
cs.IR2018
Towards a simplified ontology for better e-commerce search
Aliasgar Kutiyanawala, Prateek Verma, Zheng +1
Query Understanding is a semantic search method that can classify tokens in a customer's search query to entities such as Product, Brand, etc. This method can overcome the limitati…
cs.IR2018
End-to-End Neural Ranking for eCommerce Product Search: an application of task models and textual embeddings
Eliot Brenner, Jun Zhao, Aliasgar Kutiyanawala +1
We consider the problem of retrieving and ranking items in an eCommerce catalog, often called SKUs, in order of relevance to a user-issued query. The input data for the ranking are…