output
20122026
most citedOn Application of Learning to Rank for E-Commerce Search

143 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing 2018 · cs.IRShow all

3 papers · 2 filters

cs.IR2018★ 5 cited

Text Embeddings for Retrieval From a Large Knowledge Base

Tolgahan Cakaloglu, Christian Szegedy, Xiaowei Xu

Text embedding representing natural language documents in a semantic vector space can be used for document retrieval using nearest neighbor lookup. In order to study the feasibilit…

cs.IR2018★ 1 cited

Leveraging Catalog Knowledge Graphs for Query Attribute Identification in E-Commerce Sites

Suhas Ranganath

Millions of people use online e-commerce platforms to search and buy products. Identifying attributes in a query is a critical component in connecting users to relevant items. Howe…

cs.IR2018★ 7 cited

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…