gianluca fiorelli - the alphabet of google

GIANLUCA FIORELLI

The Alphabet of Google

@gfiorelli1

Applichiamo il metodo socratico

gfiorelli1 #theinbounder

A cosa pensa un SEO quando si parla di

Google?gfiorelli1 #theinbounder


SHAMEgfiorelli1 #theinbounder


I soliti sospetti(nulla di personale, ma…)

Inside Search Blog

Office Hours Videos

Dichiarazioni in rete

Brevetti/Studi Acquisizioni

RumoriPropaganda

Fandom

NuoviGooglers

APP Indexing

Rich Cards

Real time Indexing API

Webmaster Blog

Interstitial

AMP Series

Reviews in Local KGraph

New AMP Tester

More Https

PWA

Mobile1st

Rich Cards

New Mobile Friendly Test API

UGC Spam

Crawl Budget

Google Safe Browsing’s Site Status

#Nohacked

Similar Item

How We Fought Webspam

Research BlogPiù di 40 posts in 5 mesi e un solo grande tema gfiorelli1 #theinbounder

BrevettiSentence Compression PatentAssociating an Entity With a Search QueryMethods & Systems For Classifying Data Using a Hierarchical TaxonomyRanking EventsUser-Context-Based Search EngineRecommended News On Map With Geo EntitiesWill Google Start Giving People Social Media Influencer Scores?How Google May Rank Websites Based Upon Their Databases Answering QueriesGoogle’s Related Questions Patent or ‘People Also Ask’ QuestionsRanking Local Businesses Based Upon Quality Measures including Travel TimeGoogle May Check to See if People Go to Geographic Locations Google May RecommendGoogle Introduces a Social Where Next Suggestion PatentGoogle Search Query Refinements Patent UpdatedGoogle and Spoken Queries: Understanding Stressed PronounsA New Search Results Evaluation Model from GoogleAnswering Featured Snippets Timely, Using Sentence Compression on NewsGoogle Patents Context Vectors to Improve SearchHow Google May Respond to Reverse Engineering of Spam DetectionHow Google May Map a Query to an Entity for Suggestions gfiorelli1 #theinbounder

http://www.seobythesea.com/

Sentence Compression PatentAssociating an Entity With a Search QueryMethods & Systems For Classifying Data Using a Hierarchical TaxonomyRanking EventsUser-Context-Based Search EngineRecommended News On Map With Geo EntitiesWill Google Start Giving People Social Media Influencer Scores?How Google May Rank Websites Based Upon Their Databases Answering QueriesGoogle’s Related Questions Patent or ‘People Also Ask’ QuestionsRanking Local Businesses Based Upon Quality Measures including Travel TimeGoogle May Check to See if People Go to Geographic Locations Google May RecommendGoogle Introduces a Social Where Next Suggestion PatentGoogle Search Query Refinements Patent UpdatedGoogle and Spoken Queries: Understanding Stressed PronounsA New Search Results Evaluation Model from GoogleAnswering Featured Snippets Timely, Using Sentence Compression on NewsGoogle Patents Context Vectors to Improve SearchHow Google May Respond to Reverse Engineering of Spam DetectionHow Google May Map a Query to an Entity for Suggestions


http://www.seobythesea.com/

Entity SearchVoice SearchInformational RetrievalLocal Search / GeolocalizationSearch User Experience

Brevetti

Learning from User Interactions in Personal Search via Attribute ParameterizationRelated Event DiscoverySituational Context for Ranking in Personal SearchImproving topic clustering on search queries with word co-occurrence and bipartite graph co-clusteringIncorporating Clicks, Attention and Satisfaction into a Search Engine Result Page Evaluation ModelLarge-Scale Analysis of Viewing Behavior: Towards Measuring Satisfaction with Mobile Proactive SystemsLearning for Efficient Supervised Query Expansion via Two-stage Feature SelectionLearning to Rank with Selection Bias in Personal SearchM3A: Model, MetaModel, and Anomaly Detection in Web SearchesUsing Machine Learning to Improve the Email ExperienceWide & Deep Learning for Recommender Systems


Studi - Information Retrieval

Learning from User Interactions in Personal Search via Attribute ParameterizationRelated Event DiscoverySituational Context for Ranking in Personal SearchImproving topic clustering on search queries with word co-occurrence and bipartite graph co-clusteringIncorporating Clicks, Attention and Satisfaction into a Search Engine Result Page Evaluation ModelLarge-Scale Analysis of Viewing Behavior: Towards Measuring Satisfaction with Mobile Proactive SystemsLearning for Efficient Supervised Query Expansion via Two-stage Feature SelectionLearning to Rank with Selection Bias in Personal SearchM3A: Model, MetaModel, and Anomaly Detection in Web SearchesUsing Machine Learning to Improve the Email ExperienceWide & Deep Learning for Recommender Systems gfiorelli1 #theinbounder

Machine LearningContextPersonal SearchSearch User Experience

Studi - Information Retrieval


Generating Long and Diverse Responses with Neural Conversation ModelsLanguage Modeling in the Era of Abundant DataMultilingual Metaphor Processing: Experiments with Semi-Supervised and Unsupervised LearningA Piggyback System for Joint Entity Mention Detection and Linking in Web QueriesCollective Entity Resolution with Multi-Focal AttentionConversational Contextual Cues: The Case of Personalization and History for Response RankingExploring the Steps of Verb Phrase Ellipsis

Studi - Natural Language Processing


About 45 Papers in just 1 year!Generating Long and Diverse Responses with Neural Conversation ModelsLanguage Modeling in the Era of Abundant DataMultilingual Metaphor Processing: Experiments with Semi-Supervised and Unsupervised LearningA Piggyback System for Joint Entity Mention Detection and Linking in Web QueriesCollective Entity Resolution with Multi-Focal AttentionConversational Contextual Cues: The Case of Personalization and History for Response RankingExploring the Steps of Verb Phrase Ellipsis

Entity Search ContextRhetoric Personalization

Search User Experience

Studi - Natural Language Processing

WebPass

Moodstock

Anvato

Kifi

LaunchKit

Orbitera

Apigee

Eyefluence

AcquisizioniUrbanEngine

API.ai

Famebit

Kaggle


ImmaginiVideo Video Influencers

APP Link Sharing & Raccomandazionilocali & Geo-localizzazione

AIChatbotsRicerca Vocale & Linguaggio naturale

ML & DatasetsEntity Search Coding CollaborativoAPI

ProdottiGoogle Assistant

DayDream VR

Google Home

GBoard

Google Lens

Search User Experience

Search Experience Optimization

SEO TecnicoHTTP/2Mobile FirstAMP / PWA

SEO come MARKETINGVideo & ImmaginiRicerca Locale Semantic Search

UNDERSTANDINGINFORMATION RETRIEVALFILTERING & CLUSTERING

RANKINGPAINTING

INDEXING

CRAWLING

UNDERSTANDINGINFO RETRIEVAL

FILTERING &CLUSTERING

RANKINGPAINTING

PARSING

ML

NOML

MOBILEFIRST

HTTP/2AMPPWA

MOBILEFIRST

MOBILEFIRSTCHECKLIST

#1Check Mobile Agent / Client

Handling

#2Seguite il Modello RAIL

https://developers.google.com/web/fundamentals/performance/rail

#3Implementare i dati strutturati per Rich

Cards a Livello di Dominio e Paginahttps://youtu.be/B0BA7Tswavs

A livello di Paginahttps://youtu.be/B0BA7Tswavs

A livello di Dominiohttps://youtu.be/B0BA7Tswavs

#4Prestare attenzione alla gerarchia

delle Urls > Hreflang!!

#5Rivedete l’usabilità dei contenutiper evitare un Bounce Rate alto

https://youtu.be/DIGfwUt53nI

#6Rivedete la UX lungo tutto il

Customer Journey facendo test sumobile

SPEEDCHECKLIST

#1Speed > HTTP/2

Multiplexed resources Browser to ServerPrioritized by type & context Keep-Alive by default

https://developers.google.com/web/fundamentals/performance/http2/

#2Speed Front-End with Image

Spriting

#3Gestione dei tag - Use Tag Assistant

extension for Chromehttp://itseo.org/tagasstnt

#4Usate JSON-LD per i Dati Strutturatihttps://moz.com/blog/using-google-tag-manager-to-dynamically-generate-

schema-org-json-ld-tags

#6Service Workers - PWA

CONTESTO

Pensare la Local search solo come MyBusiness potrebbe limitare le

opportunità che le aziende hanno di ottenere visibità e traffico organico.

GBoard

CONTESTO

CONTESTO

PARSING

SEMANTICA

@gfiorelli1

RicercaPersonalizzata

@gfiorelli1

PARSINGUNDERSTANDING

RELEVANCYQUALITY

QUALITY

NON “BEI TESTI”

QUALITYMA UN MIX DI:

• Coerenza tra pagina e intenzione di ricercatargetizzata


• Capacità di offrire rispostealle domande esplicite edesplicite


• Facilità d’uso, soprattutto da Mobile

QUALITY

MA UN MIX DI:

• In un ambiente sicuro

Video & Immagini

Uno degli eroi dei miei figli

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.

Similar items: Rich products feature on

Google Image Search

Google Lens

https://www.google.com/intl/en/about/

Generico (ma non triviale) conoscimento di tutte le discipline del marketing online

TECHNICAL

MARKETERS

http://safecont.com/

https://research.google.com/teams/nlu/

Buyer Persona

KW Research

Top 10 Ranking Sites

* Query Intention

Entity Recognition via CNL API

ThesaurusCreation

gianluca fiorelli - the alphabet of google

Marketing