First assessment of learning-to-rank: testing machine-learned ranking of search results on English Wikipedia
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Updated
Oct 10, 2017 - R
First assessment of learning-to-rank: testing machine-learned ranking of search results on English Wikipedia
A quick hack, for now, to recollect expert based judgmenet for search
3rd Place Solution for HomeDepot Product Search Results Relevance Competition on Kaggle.
Search Relevance Surveys and Deep Learning: Turning Noisy, Crowd-sourced Opinions Into An Accurate Relevance Judgement (T175048)
Solr Relevance Ranking Analysis and Visualization Tool
1st Place Solution for CrowdFlower Product Search Results Relevance Competition on Kaggle.
In this repo, I attempt to quantify the search relevance of different query settings using the Normalized Discounted Cumulative Gain (NDCG).
The 3rd place solution code for the Wikipedia - Image/Caption Matching Competition on Kaggle
Measure relevance of search result for CrowdFlower, an ecomerce site. Model trained was SVC
Testing tool to verify the search qualities of the Elasticsearch indices
An open source tool to measure search relevance.
Search relevancy algorithm for news articles using Sentence-BERT model and ANNOY library along with deployment on AWS using Docker.
Exploring search relevance techniques.
Query preprocessor for Java-based search engines (Querqy Core and Solr implementation)
Tools to help search relevance engineers and business users tune search results for their OpenSearch applications.
Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch
Framework for building Commerce Search Solutions around open source search technology like Elasticsearch
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