Framework for building Commerce Search Solutions around open source search technology like Elasticsearch
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Updated
Jun 25, 2024 - Java
Framework for building Commerce Search Solutions around open source search technology like Elasticsearch
Query preprocessor for Java-based search engines (Querqy Core and Solr implementation)
Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch
Tools to help search relevance engineers and business users tune search results for their OpenSearch applications.
Exploring search relevance techniques.
Search relevancy algorithm for news articles using Sentence-BERT model and ANNOY library along with deployment on AWS using Docker.
An open source tool to measure search relevance.
Testing tool to verify the search qualities of the Elasticsearch indices
Measure relevance of search result for CrowdFlower, an ecomerce site. Model trained was SVC
The 3rd place solution code for the Wikipedia - Image/Caption Matching Competition on Kaggle
In this repo, I attempt to quantify the search relevance of different query settings using the Normalized Discounted Cumulative Gain (NDCG).
1st Place Solution for CrowdFlower Product Search Results Relevance Competition on Kaggle.
Solr Relevance Ranking Analysis and Visualization Tool
Search Relevance Surveys and Deep Learning: Turning Noisy, Crowd-sourced Opinions Into An Accurate Relevance Judgement (T175048)
3rd Place Solution for HomeDepot Product Search Results Relevance Competition on Kaggle.
A quick hack, for now, to recollect expert based judgmenet for search
First assessment of learning-to-rank: testing machine-learned ranking of search results on English Wikipedia
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