Awesome Search - this is all about the (e-commerce, but not only) search and its awesomeness

Systematic Review Query Visualisation and Understanding Interface

IntentsKB: A Knowledge Base of Entity-Oriented Search Intents - CIKM'18

Towards an Understanding of Entity-Oriented Search Intents - ECIR'18

Presentation and Code for talk at Conferences - MLDS-2020 and DHS-2019

Target Type Identification for Entity-Bearing Queries - SIGIR'17

A new package that processes user queries about why small voting or ranking projects get flagged as spam so easily. It uses natural language processing to understand the input and generates a structur

A reference implementation of modern search architecture that prioritizes deterministic intent enforcement (BM25 + boosts), bounded semantic expansion, and explainable ranking. Built to reflect real production search systems rather than end-to-end black-box ML.

A robust Retrieval-Augmented Generation (RAG) system for noisy, multi-intent queries using LLM-based query understanding. Implemented in Python with PostgreSQL and OpenSearch for retrieval and storage.

🔍 Process user queries about spam flags on small voting projects with natural language insights and structured responses.