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Semantic Search Features and Benefits

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Reference results

Semantically annotated results

Full-text similarity search

Search on semantic/syntactic annotations

Concept search

Ontology-based search

Semantic Web search

Faceted search

Clustered search

Natural language search

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Practical search features based on semantic search technology:

 

 

 

Semantic Web for Business

 

The most immediate challenge faced by businesses is the need to optimize their websites for semantic search.

Businesses need to think about the issue of context when creating content on their websites.

Page titles and keywords used in content should be selected for their ability to help search engines match the information with a user’s intent or the context of the search.

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A Search for Meaning—Web 3.0 and Semantic Technology: Questions

List five different practical ways that semantic technology is enhancing the search experience of users.

How do metadata tags facilitate more accurate search results?

Briefly describe the three phases of the World Wide Web’s development.

Define the words “context,” “personalization,” and “vertical search.” Explain how they make for better information search results.

What are three languages developed by the W3C and associated with the Semantic Web?

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Learning Objectives (5 of 5)

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Web 2.0 Technologies

 

 

Using Search Technology for Business Success

 

 

A Search for Meaning—Web 3.0 and Semantic

Technology

 

 

Recommendation Engines

 

 

Social Web Tools and Applications

 

 

 

 

Recommendation Engines

 

Three widely used approaches to creating useful recommendations:

content-based filtering,

collaborative filtering, and

hybrid strategies

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Content-based filtering

Recommends products based on the product features of items the customer has interacted with in the past.

 

 

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Collaborative filtering

Recommendations based on a user’s similarity to other people.

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Other Approaches to Recommendation Engines

 

Knowledge-based systems use information about a user’s needs to recommend products.

Demographic systems base recommendations on demographic factors corresponding to a potential customer (i.e., age, gender, race, income, etc.).

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Limitations of Recommendation Engines

 

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Cold start or new user

 

 

Sparsity

 

 

Limited feature content

 

 

Overspecialization

 

 

 

 

Hybrid recommendation engines

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Recommendations based on some combination of the methodologies (content-based filtering, collaboration filtering, knowledge-based and demographic systems):

Weighted hybrid

Mixed hybrid

Cascade hybrid

Compound hybrid

 

Recommendation Engines: Questions

How is a recommendation engine different from a search engine?

Besides e-commerce websites that sell products, what are some other ways that recommendation engines are being used on the Web today?

What are some examples of user information required by recommendation engines that use collaborative filtering?

Before implementing a content-based recommendation engine, what kind of information would website operators need to collect about their products?

What are the four limitations or challenges that recommendation systems sometimes face?

What is a recommendation engine called that combines different methodologies to create recommendations? What are three ways these systems combine methodologies?

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Copyright

Copyright © 2021 John Wiley & Sons, Inc.

All rights reserved. Reproduction or translation of this work beyond that permitted in Section 117 of the 1976 United States Act without the express written permission of the copyright owner is unlawful. Request for further information should be addressed to the Permissions Department, John Wiley & Sons, Inc. The purchaser may make back-up copies for his/her own use only and not for distribution or resale. The Publisher assumes no responsibility for errors, omissions, or damages, caused by the use of these programs or from the use of the information contained herein.

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