Introduction to Learning to Rank with Python
A formal definition of learning to rank from A Short Introduction to Learning to Rank by Hang Li is as follows:
Recent posts
Understanding PageRank
Query-dependent ranking models such as TF-IDF and BM25 are not sufficient for web search because they mainly measure how closely the terms in a query match t...
Relevance Feedback and Query Expansion
The problem of a pure keyword matching approach is that the systems can’t handle synonymy issue. For example, you might search for “python” meaning the progr...
Langauge Models for Information Retrieval
In information retrieval (henceforth IR), the language modeling approach is a form of probabilistic retrieval. Unlike traditional probabilistic methods, whic...
Virtualization
In the 1960s, early computers like IBM mainframes typically ran only one operating system at a time. This design had several drawbacks: Different workload...
I/O Management
A core responsibility of an operating system is managing the computer’s hardware. This responsibility covers not only the CPU and memory, but also input/outp...
Deploying Self-managed Elasticsearch 8 Cluster with Docker on AWS
Deploying a self-managed Elasticsearch cluster for production is a bit of pain. It’s not just about understanding the core components. You also have to manua...
Systematic Evaluation for Information Retrieval: MAP and nDCG
The effectiveness (or accuracy) of an information retrieval system refers to how well it ranks relevant documents higher than non-relevant ones. Evaluating t...
Understanding BM25 in Text Retrieval
When you type a query into a search engine, it finds documents that contain your keywords. The problem is: how do you order those documents from most to leas...
Building Korean Query Auto-completion using Elasticsearch
Query auto-completion (QAC) is a feature that suggests completions for a user’s input query. It tries to guess what you’re about to type and throws out sugge...