15. Research
Blog
More than 40 posts in 5
months.
More posts than the
Webmaster Blog.
And one clear main topic
gfiorelli1 #theinbounder
16. Patents & Papers
Sentence Compression Patent
Associating an Entity With a Search Query
Methods & Systems For Classifying Data Using a Hierarchical Taxonomy
Ranking Events
User-Context-Based Search Engine
Recommended News On Map With Geo Entities
Will Google Start Giving People Social Media Influencer Scores?
How Google May Rank Websites Based Upon Their Databases Answering Queries
Google’s Related Questions Patent or ‘People Also Ask’ Questions
Ranking Local Businesses Based Upon Quality Measures including Travel Time
Google May Check to See if People Go to Geographic Locations Google May Recommend
Google Introduces a Social Where Next Suggestion Patent
Google Search Query Refinements Patent Updated
Google and Spoken Queries: Understanding Stressed Pronouns
A New Search Results Evaluation Model from Google
Answering Featured Snippets Timely, Using Sentence Compression on News
Google Patents Context Vectors to Improve Search
How Google May Respond to Reverse Engineering of Spam Detection
How Google May Map a Query to an Entity for Suggestions
gfiorelli1 #theinbounder
http://www.seobythesea.com/
17. Patents & Papers
Sentence Compression Patent
Associating an Entity With a Search Query
Methods & Systems For Classifying Data Using a Hierarchical Taxonomy
Ranking Events
User-Context-Based Search Engine
Recommended News On Map With Geo Entities
Will Google Start Giving People Social Media Influencer Scores?
How Google May Rank Websites Based Upon Their Databases Answering Queries
Google’s Related Questions Patent or ‘People Also Ask’ Questions
Ranking Local Businesses Based Upon Quality Measures including Travel Time
Google May Check to See if People Go to Geographic Locations Google May Recommend
Google Introduces a Social Where Next Suggestion Patent
Google Search Query Refinements Patent Updated
Google and Spoken Queries: Understanding Stressed Pronouns
A New Search Results Evaluation Model from Google
Answering Featured Snippets Timely, Using Sentence Compression on News
Google Patents Context Vectors to Improve Search
How Google May Respond to Reverse Engineering of Spam Detection
How Google May Map a Query to an Entity for Suggestions
gfiorelli1 #theinbounder
http://www.seobythesea.com/
Entity Search
Local Search / Geolocalization
Informational Retrieval (old patents
updated)
Vocal Search
Search User Experience
18. Patents & Papers
Information Retrieval
Learning from User Interactions in Personal Search via Attribute Parameterization
Related Event Discovery
Situational Context for Ranking in Personal Search
Improving topic clustering on search queries with word co-occurrence and bipartite
graph co-clustering
Incorporating Clicks, Attention and Satisfaction into a Search Engine Result Page
Evaluation Model
Large-Scale Analysis of Viewing Behavior: Towards Measuring Satisfaction with
Mobile Proactive Systems
Learning for Efficient Supervised Query Expansion via Two-stage Feature Selection
Learning to Rank with Selection Bias in Personal Search
M3A: Model, MetaModel, and Anomaly Detection in Web Searches
Using Machine Learning to Improve the Email Experience
Wide & Deep Learning for Recommender Systems gfiorelli1 #theinbounder
19. Learning from User Interactions in Personal Search via Attribute Parameterization
Related Event Discovery
Situational Context for Ranking in Personal Search
Improving topic clustering on search queries with word co-occurrence and bipartite
graph co-clustering
Incorporating Clicks, Attention and Satisfaction into a Search Engine Result Page
Evaluation Model
Large-Scale Analysis of Viewing Behavior: Towards Measuring Satisfaction with
Mobile Proactive Systems
Learning for Efficient Supervised Query Expansion via Two-stage Feature Selection
Learning to Rank with Selection Bias in Personal Search
M3A: Model, MetaModel, and Anomaly Detection in Web Searches
Using Machine Learning to Improve the Email Experience
Wide & Deep Learning for Recommender Systems gfiorelli1 #theinbounder
Search User Experience
Personalised Search
Context
Patents & Papers
Information Retrieval
20. Patents & Papers
Natural Language Processing
gfiorelli1 #theinbounder
About 45 Papers in just 1 year!
Generating Long and Diverse Responses with Neural Conversation Models
Language Modeling in the Era of Abundant Data
Multilingual Metaphor Processing: Experiments with Semi-Supervised and
Unsupervised Learning
A Piggyback System for Joint Entity Mention Detection and Linking in Web
Queries
Collective Entity Resolution with Multi-Focal Attention
Conversational Contextual Cues: The Case of Personalization and History for
Response Ranking
Exploring the Steps of Verb Phrase Ellipsis
21. Patents & Papers
Natural Language Processing
gfiorelli1 #theinbounder
About 45 Papers in just 1 year!
Generating Long and Diverse Responses with Neural Conversation Models
Language Modeling in the Era of Abundant Data
Multilingual Metaphor Processing: Experiments with Semi-Supervised and
Unsupervised Learning
A Piggyback System for Joint Entity Mention Detection and Linking in Web
Queries
Collective Entity Resolution with Multi-Focal Attention
Conversational Contextual Cues: The Case of Personalization and History for
Response Ranking
Exploring the Steps of Verb Phrase Ellipsis
Rhetoric
Entity Search
Context
Personalisation
32. 1.The average age kids start owning
a smartphone is 10.3 years;
2.Children from 5 to 13 years old
(and also young people up to 20
years old) tend to me more visual
than textual;
3.Their influence on the buying
habits of their parents has been
known for many years and, in 2012,
it was equal to $1.2 trillion USD in
spending.
48. Checklist:
• Identify the Entities related to our niche and how they are
connected
• Match them with our Audience interests
49. Checklist:
• Identify the Entities related to our niche and how they are
connected
• Match them with our Audience interests
• Content Architecture based on Ontology/Taxonomy based
Hubs
50. Checklist:
• Identify the Entities related to our niche and how they are
connected
• Match them with our Audience interests
• Content Architecture based on Ontology/Taxonomy based
Hubs
• Do Keywords Research and Mapping with Entities in mind