Domain Model Ve
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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.
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Domain Model Ve
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.
Domain Model Ve
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.
Domain Model Ve
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.
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Este documento describe diferentes tipos de signos gráficos como marcas, símbolos, pictogramas y sus características. Explica que las marcas deben ser simples, memorables, claras y asociadas al producto. También habla de diferentes orígenes de nombres de marcas como nombres propios, animales, lugares geográficos y acrónimos. Finalmente, distingue entre signos icónicos, esquemáticos y abstractos según su grado de parecido con la realidad.
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Domain Model Ve
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.
Domain Model Ve
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.
Domain Model Ve
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.
Imagen Tema 4
Este documento describe diferentes tipos de signos gráficos como marcas, símbolos, pictogramas y sus características. Explica que las marcas deben ser simples, memorables, claras y asociadas al producto. También habla de diferentes orígenes de nombres de marcas como nombres propios, animales, lugares geográficos y acrónimos. Finalmente, distingue entre signos icónicos, esquemáticos y abstractos según su grado de parecido con la realidad.
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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.
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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.
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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.
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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.
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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.
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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.
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Los alumnos tienen diferentes opiniones sobre la escuela, los profesores y los compañeros. Algunas cosas que no les gustan de los profesores son que son estrictos, dejan mucha tarea y no los comprenden, mientras que les gusta cuando los profesores saben enseñar y dan consejos. De los compañeros, a veces no les gusta su comportamiento o actitud. Sobre la escuela, les molesta tener muchas materias y el reglamento estricto, pero les agradan las horas libres y la convivencia con otros grupos. Para que los alumnos
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La historia cuenta brevemente sobre una chica que le pregunta a un chico si quiere casarse con ella, a lo que él responde que no. Como resultado, la chica vive feliz y libre, sin responsabilidades domésticas, saliendo con amigas y gastando su dinero en sí misma. El autor sugiere que este tipo de cuento alternativo habría sido mejor para las niñas que los tradicionales cuentos de hadas.
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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.
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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.
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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.
Sequence Diagram
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.
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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.
Sequence Diagram V6
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.
Domain Model V2
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.
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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.
Sequence Diagram V5
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.
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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.
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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.
Sequence Diagram
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Domain Model Ve
- 1. Comes-from 1 Task 1 Contains 1 1 * 1 Creates 1 1 Contains * Query Recommendation Page 1 1 1 1 1 1 Produced-by Evaluation 1 Produced-by 1 1 forms QueryFormulator SearchEngine