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AI Powered Search > Parte 1 > Flashcards

Parte 1 Flashcards

1
Q

Define “Content Understanding”

A

The ability to find any arbitrary content leveraging keywords and matching on attributes

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2
Q

The ability to find any arbitrary content leveraging keywords and matching on attributes

A

Define “Content Understanding”

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3
Q

Define “User Understanding”

A

The ability to understand each user’s specific preferences and leverage those to return more personalized results

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4
Q

The ability to understand each user’s specific preferences and leverage those to return more personalized results

A

Define “User Understanding”

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5
Q

Define “Domain Understanding”

A

The ability to interpret words, phrases, concepts, entities, and nuanced interpretations and relationships between each of these within you own domain-specific context

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6
Q

The ability to interpret words, phrases, concepts, entities, and nuanced interpretations and relationships between each of these within you own domain-specific context

A

Define “Domain Understanding”

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7
Q

What are the three dimensions of user intent?

A
  • Content Understanding
  • User Understanding
  • Domain Understanding
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8
Q
  • Content Understanding
  • User Understanding
  • Domain Understanding
A

What are the three dimensions of user intent?

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9
Q

Summarize the end goal of AI-powered search

A

The optimal understanding of users and their query intent

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10
Q

The optimal understanding of users and their query intent

A

The end goal of AI-powered search

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11
Q
  1. Basic Keyword Search
  2. Taxonomies / Entity Extraction
  3. Query Intent
  4. Automated Relevance Tuning
  5. Self Learning
A

Search Intelligence Progression steps

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12
Q

Search Intelligence Progression steps

A
  1. Basic Keyword Search
  2. Taxonomies / Entity Extraction
  3. Query Intent
  4. Automated Relevance Tuning
  5. Self Learning
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13
Q

Basic Keyword Search: Step in Search Intelligence Progression steps and characteristics

A

Step 1
- Inverted index
- TF-IDF
- BM25
- Multiligual text analysis
- Query formulation

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14
Q
  • Inverted index
  • TF-IDF
  • BM25
  • Multiligual text analysis
  • Query formulation
A

Basic Keyword Search: Step 1 of Search Intelligence Progression steps

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15
Q

Taxonomies / Entity Extraction: Step in Search Intelligence Progression steps and characteristics

A

Step 2
- Entity Recognition
- Taxonomies
- Ontologies
- Business Rules
- Synonyms

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16
Q
  • Entity Recognition
  • Taxonomies
  • Ontologies
  • Business Rules
  • Synonyms
A

Taxonomies / Entity Extraction: Step 2 of Search Intelligence Progression steps

17
Q

Query Intent: Step in Search Intelligence Progression steps and characteristics

A

Step 3
- Query Classification
- Semantic Query Parsing
- Semantic Knowledge Graphs
- Concept Expansion
- Automatic Query Rewrites
- Clustering
- Classification
- Personalization
- Question/Answer Systems
- Virtual Assistants

18
Q
  • Query Classification
  • Semantic Query Parsing
  • Semantic Knowledge Graphs
  • Concept Expansion
  • Automatic Query Rewrites
  • Clustering
  • Classification
  • Personalization
  • Question/Answer Systems
  • Virtual Assistants
A

Query Intent: Step 3 in Search Intelligence Progression steps

19
Q

Automated Relevancy Tuning: Step in Search Intelligence Progression steps and characteristics

A

Step 4
- Signal Boosting
- Collaborative Filtering
- AB Testing / Multi-armed bandits / Backtesting
- Genetic Algorithms
- Learning to Rank

20
Q
  • Signal Boosting
  • Collaborative Filtering
  • AB Testing / Multi-armed bandits / Backtesting
  • Genetic Algorithms
  • Learning to Rank
A

Automated Relevancy Tuning: Step 4 in Search Intelligence Progression steps

21
Q

Self-learning: Step in Search Intelligence Progression steps

A

Step 5, final step

22
Q

Step 5 in Search Intelligence Progression steps

A

Self-learning: Final step

23
Q

Reflected Intelligence

A

Leveraging continual feedback loops of user inputs, content updates, and user interactions with content in order to continually learn and improve the quality of your search application.

24
Q

Leveraging continual feedback loops of user inputs, content updates, and user interactions with content in order to continually learn and improve the quality of your search application.

A

Reflected Intelligence

25
Q

What are the two extreme ends of a continuous personalization
spectrum within information retrieval

A

Search and Recommendation

26
Q

What spectrum are Search and Recommendation the extremes of?

A

The two extreme ends of a continuous personalization
spectrum within information retrieval

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