Customising Ranking Models for Enterprise Search on Bilingual Click-Through Dataset

12/23/2021
by   Gizem Gezici, et al.
0

In this work, we provide the details about the process of establishing an end-to-end system for enterprise search on bilingual click-through dataset. The first part of the paper will be about the high-level workflow of the system. Then, in the second part we will elaborately mention about the ranking models to improve the search results in the vertical search of the technical documents in enterprise domain. Throughout the paper, we will mention the way which we combine the methods in IR literature. Finally, in the last part of the paper we will report our results using different ranking algorithms with NDCG@k where k is the cut-off value.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/19/2018

End-to-End Neural Ranking for eCommerce Product Search: an application of task models and textual embeddings

We consider the problem of retrieving and ranking items in an eCommerce ...
research
04/15/2019

An Axiomatic Approach to Regularizing Neural Ranking Models

Axiomatic information retrieval (IR) seeks a set of principle properties...
research
03/27/2022

piRank: A Probabilistic Intent Based Ranking Framework for Facebook Search

While numerous studies have been conducted in the literature exploring d...
research
03/26/2018

Demystifying Core Ranking in Pinterest Image Search

Pinterest Image Search Engine helps hundreds of millions of users discov...
research
08/29/2023

Vector Search with OpenAI Embeddings: Lucene Is All You Need

We provide a reproducible, end-to-end demonstration of vector search wit...
research
05/26/2023

The Search for Stability: Learning Dynamics of Strategic Publishers with Initial Documents

We study a game-theoretic model of information retrieval, in which strat...
research
05/07/2020

Learning Robust Models for e-Commerce Product Search

Showing items that do not match search query intent degrades customer ex...

Please sign up or login with your details

Forgot password? Click here to reset