Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Enhancements in Search Functionality

Description

This lecture introduces a new search feature added to the Coupageur tool, allowing users to search within people's profiles in addition to by name. The instructor demonstrates how the search engine works, showing examples of searching for specific information and discussing its practicality.

Official source

Related concepts (4)

Information retrieval

Information retrieval (IR) in computing and information science is the process of obtaining information system resources that are relevant to an information need from a collection of those resources. Searches can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.

Relevance (information retrieval)

In information science and information retrieval, relevance denotes how well a retrieved document or set of documents meets the information need of the user. Relevance may include concerns such as timeliness, authority or novelty of the result. The concern with the problem of finding relevant information dates back at least to the first publication of scientific journals in the 17th century. The formal study of relevance began in the 20th Century with the study of what would later be called bibliometrics.

Search engine

A search engine is a software system that finds web pages that match a web search. They search the World Wide Web in a systematic way for particular information specified in a textual web search query. The search results are generally presented in a line of results, often referred to as search engine results pages (SERPs). The information may be a mix of hyperlinks to web pages, images, videos, infographics, articles, and other types of files. Some search engines also mine data available in databases or open directories.

Multimedia information retrieval

Multimedia information retrieval (MMIR or MIR) is a research discipline of computer science that aims at extracting semantic information from multimedia data sources. Data sources include directly perceivable media such as audio, and video, indirectly perceivable sources such as text, semantic descriptions, biosignals as well as not perceivable sources such as bioinformation, stock prices, etc. The methodology of MMIR can be organized in three groups: Methods for the summarization of media content (feature extraction).

Related lectures (4)

Indexing and Distributed Retrieval

Explores indexing techniques, inverted files, map-reduce algorithms, and top-k document retrieval methods in text retrieval systems.

Information Retrieval Basics Log in to Mediaspace to watch this video

Introduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.

Information Retrieval: Indexing and Retrieval Log in to Mediaspace to watch this video

Covers indexing techniques, distributed retrieval algorithms, and challenges in large-scale web indexing.

Knowledge Representation: Semantics and Data Structures Log in to Mediaspace to watch this video

Explores knowledge representation, data structures, semantics, and the challenges of searching for data on the web.

Related concepts (4)

Information retrieval

Relevance (information retrieval)

Search engine

Multimedia information retrieval