Semantic Search Features and Benefits
Related searches/queries
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.
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