A quick hack, for now, to recollect expert based judgmenet for search
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Updated
Jun 4, 2018 - Ruby
A quick hack, for now, to recollect expert based judgmenet for search
Measure relevance of search result for CrowdFlower, an ecomerce site. Model trained was SVC
In this repo, I attempt to quantify the search relevance of different query settings using the Normalized Discounted Cumulative Gain (NDCG).
First assessment of learning-to-rank: testing machine-learned ranking of search results on English Wikipedia
Search relevancy algorithm for news articles using Sentence-BERT model and ANNOY library along with deployment on AWS using Docker.
Exploring search relevance techniques.
Search Relevance Surveys and Deep Learning: Turning Noisy, Crowd-sourced Opinions Into An Accurate Relevance Judgement (T175048)
The 3rd place solution code for the Wikipedia - Image/Caption Matching Competition on Kaggle
An open source tool to measure search relevance.
Testing tool to verify the search qualities of the Elasticsearch indices
Solr Relevance Ranking Analysis and Visualization Tool
Framework for building Commerce Search Solutions around open source search technology like Elasticsearch
Tools to help search relevance engineers and business users tune search results for their OpenSearch applications.
Query preprocessor for Java-based search engines (Querqy Core and Solr implementation)
3rd Place Solution for HomeDepot Product Search Results Relevance Competition on Kaggle.
Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch
1st Place Solution for CrowdFlower Product Search Results Relevance Competition on Kaggle.
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