Dice.com's relevancy feedback solr plugin created by Simon Hughes (Dice). Contains request handlers for doing MLT style recommendations, conceptual search, semantic search and personalized search
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Updated
May 12, 2021 - Java
Dice.com's relevancy feedback solr plugin created by Simon Hughes (Dice). Contains request handlers for doing MLT style recommendations, conceptual search, semantic search and personalized search
Automatic Query Image Disambiguation (AID)
A search engine bases on the course Information Retrieval at BML Munjal University. It includes features like relevance feedback, pseudo relevance feedback, page rank, hits analysis, document clustering.
A Convenient Field Study Toolkit (for our WWW21 paper Towards a Better Understanding of Query Reformulation Behavior in Web Search)
Implementation of Probabilistic Retrieval Query expansion and Relevance Model based Language Modelling aimed at improving the precision of results using pseudo-relevance feedback in Information Retrieval.
Compilation of Information Retrieval codes.
A Python implementation of Paper "Image Retrieval with Relevance Feedback using SVM Active Learning"
Official implementation of "Confidence-Aware Active Feedback for Interactive Instance Search".
The repository contains a subset of the ReDial dataset with additional annotations on relevance and usefulness created in the paper: Context Does Matter: Implications for Crowdsourced Evaluation Labels in Task-Oriented Dialogue Systems, accepted at NAACL'24
A search engine that employs the Rocchio Algorithm to improve result relevance.
Created various Information Retrival Algorithms from scratch in python
Information Retrieval, Information Extraction and Data Mining projects
Implementation of Relevance Feedback with Rocchio Algorithm in order to improve results in Information Retrieval. Proposal of an improvement to the Rocchio Algorithm.
Vector space modeling of MovieLens & IMDB movie data
Ceng596 Information Retrieval Course Project, InfoNinjas
This project implements advanced image retrieval techniques for Caltech101 dataset, using algorithms like MDS, SVD, DBScan, and Locality Sensitive Hashing (LSH). It focuses on optimizing retrieval accuracy and efficiency through dimensionality reduction, clustering, classification, and relevance feedback, enhancing multimedia search capabilities.
Application and evaluation of relevance feedback and thesaurus
Iterative Relevance Feedback based Answer Passage Retrieval
Applying several strategies like tf-idf, posting index, vector similarity, etc. to build an Information Retrieval system to increase the efficacy of querying the dataset.
Advanced DB Project 1
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