El correo electrónico menciona a tres personas - Joaquín, Magali y Leandro - y señala que es el Día de la Mujer, posiblemente compartiendo imágenes relacionadas o de celebración.
El correo electrónico menciona a tres personas - Joaquín, Magali y Leandro - y señala que es el Día de la Mujer, posiblemente compartiendo imágenes relacionadas o de celebración.
El documento presenta varios problemas relacionados con conceptos de capacidad, inventarios, productividad y programación lineal. En el primer problema de capacidad, se pide calcular la capacidad necesaria para producir 1000 unidades con una utilización del 80% y un rendimiento del 75%. En el segundo, se pide calcular la cantidad de máquinas necesarias para producir 900,000 unidades con una capacidad efectiva de 34 unidades por máquina, un rendimiento del 60% y una utilización del 70%. Los problemas de inventarios y productividad involucran cálculos de costos, demanda
Este documento trata sobre las redes sociales virtuales. Brevemente describe la historia y características de las redes sociales, incluyendo ventajas como la comunicación y desventajas como la privacidad. También cubre delitos informáticos y reglas para proteger la privacidad en redes sociales.
This document does not contain any substantive information to summarize. It consists of random characters without any discernible meaning or context. A meaningful summary cannot be generated from this input.
O documento apresenta quatro atividades sobre prismas. A primeira atividade compara o volume de dois prismas (paralelepípedo reto e oblíquo) e conclui que o formato oblíquo economiza cerca de 2,7% de papelão. A segunda atividade calcula a diagonal de três prismas usando o Teorema de Pitágoras. A terceira atividade calcula o volume de três prismas e conclui que o prisma hexagonal regular maximiza o volume. A quarta atividade propõe uma expressão geral para calc
This document summarizes a web recommender project. It describes three algorithms - baseline, HTML structure-based, and semantics-based - for recommending relevant web pages based on an input page. An evaluation of the algorithms on five topics found the structure-based algorithm performed best overall, particularly for the topic of "entropy". Error analysis revealed ways to improve the semantic algorithm, such as combining title words with named entities. The project demonstrated the importance of topic on recommendation success and provided insights into developing effective web recommending tools.
This document summarizes a final report on a web recommender system project. It outlines the motivation, goals, requirements, design, algorithms, evaluation, results, techniques used, and lessons learned from the project. The project aimed to build a framework for web recommendation that provides basic algorithms and evaluation methods. It designed and implemented three recommendation algorithms and conducted an evaluation with five topics and three algorithms using modified average precision. The evaluation revealed topics strongly influenced results and further analysis of algorithms is needed.
El documento presenta varios problemas relacionados con conceptos de capacidad, inventarios, productividad y programación lineal. En el primer problema de capacidad, se pide calcular la capacidad necesaria para producir 1000 unidades con una utilización del 80% y un rendimiento del 75%. En el segundo, se pide calcular la cantidad de máquinas necesarias para producir 900,000 unidades con una capacidad efectiva de 34 unidades por máquina, un rendimiento del 60% y una utilización del 70%. Los problemas de inventarios y productividad involucran cálculos de costos, demanda
Este documento trata sobre las redes sociales virtuales. Brevemente describe la historia y características de las redes sociales, incluyendo ventajas como la comunicación y desventajas como la privacidad. También cubre delitos informáticos y reglas para proteger la privacidad en redes sociales.
This document does not contain any substantive information to summarize. It consists of random characters without any discernible meaning or context. A meaningful summary cannot be generated from this input.
O documento apresenta quatro atividades sobre prismas. A primeira atividade compara o volume de dois prismas (paralelepípedo reto e oblíquo) e conclui que o formato oblíquo economiza cerca de 2,7% de papelão. A segunda atividade calcula a diagonal de três prismas usando o Teorema de Pitágoras. A terceira atividade calcula o volume de três prismas e conclui que o prisma hexagonal regular maximiza o volume. A quarta atividade propõe uma expressão geral para calc
This document summarizes a web recommender project. It describes three algorithms - baseline, HTML structure-based, and semantics-based - for recommending relevant web pages based on an input page. An evaluation of the algorithms on five topics found the structure-based algorithm performed best overall, particularly for the topic of "entropy". Error analysis revealed ways to improve the semantic algorithm, such as combining title words with named entities. The project demonstrated the importance of topic on recommendation success and provided insights into developing effective web recommending tools.
This document summarizes a final report on a web recommender system project. It outlines the motivation, goals, requirements, design, algorithms, evaluation, results, techniques used, and lessons learned from the project. The project aimed to build a framework for web recommendation that provides basic algorithms and evaluation methods. It designed and implemented three recommendation algorithms and conducted an evaluation with five topics and three algorithms using modified average precision. The evaluation revealed topics strongly influenced results and further analysis of algorithms is needed.
The document describes an algorithm for searching and recommending pages. It creates a YahooSearch object and uses it to recommend pages, parse queries, stem terms, and filter terms to create a list of relevant pages matching the search query. Key components include a QueryFormulator, Stemmer, FrequencyFilter and StructureFeatureRecommender.
The document describes a system for recommending related pages based on structure and frequency analysis. It parses pages to extract features, stems terms, filters by frequency, and forms queries to search for and return related pages. Key components include a parser, stemmer, frequency filter, query formulator, and search engine.
This document describes a recommendation system that takes a query as input, formulates it, sends it to a search engine, evaluates the results, and provides a recommendation. The recommendation is produced by a query formulator and search engine and contains the top result, its URL, and a score.
The document describes a system that takes a query as input, formulates it, and uses a search engine to find relevant pages and produce a recommendation. The search engine evaluates pages and the recommendation is produced by both the query formulation and the search engine evaluation of pages.
The document describes a recommendation system with three main components: a query formulator that creates queries from user tasks, a search engine that searches for and returns recommendations based on the queries, and an evaluator that assesses the recommendations and provides feedback to improve the system.
This document describes the relationships between different entities involved in a search and recommendation system, including queries, pages, and various processes like search engines and recommendation algorithms. A query is formulated by a user and processed by a search engine to return relevant pages. The search engine and recommendation algorithms work together to evaluate pages and provide personalized recommendations to the user.
The document describes a domain model that involves a search engine. The search engine takes key terms from a query term filter and generates a query string. It then sends the query string to a query formulator and web recommender to recommend a title and URL based on the search terms.
The document describes a search engine process that includes parsing pages, stemming terms, filtering key terms, and using those terms to recommend related pages to the user based on their search query. A YahooSearch object is created to handle the search. Pages are parsed and stemmed to extract terms, which are then filtered into key terms. The key terms are used to request and return a list of recommended pages to the user.
The document describes relationships between various entities in a domain model for evaluating search engine and recommender system outputs. It shows that a query string is input to a search engine which utilizes a query formulator and outputs key terms and pages that are then sent to and scored by the domain model. The domain model also generates evaluation results from pages, user profiles, and relationships between pages and features.
This document outlines different classes and interfaces for web recommendation and search engine functionality. It includes classes for search engines, query formulation, stopword removal, stemming, HTML parsing and stripping, and different types of recommenders that take in pages and return other relevant pages. Interfaces are defined for search engines, web recommenders, and query term filtering.
This document describes classes and methods for a search engine system. It includes classes for creating a Yahoo search object, recommending pages, searching with queries, parsing pages, stemming parsing results, filtering query terms, and classes for a recommender, controller, query formulator, HTML parser and stemmer.
The document describes an algorithm for search engine recommendation that creates objects like a YahooSearch instance, parses web pages to extract features and stem terms, and uses the stemmed terms to recommend relevant pages for a given query. It lists the classes involved like YahooSearch, QueryFormulator, HTMLParser, and Stemmer.
This document outlines different types of recommenders and search engines. It defines interfaces for a web recommender and search engine. It then lists implementations of recommenders like structure feature and semantic feature recommenders. It also lists implementations of search engines like YahooSearch, GoogleSearch, and a custom MySearch engine.
A search engine recommends pages to users based on extracted page features. It creates a structure feature recommender that takes in a page and recommends it. The search engine performs a Yahoo search with the query and returns a list of pages.