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Gianluca Fiorelli - The Alphabet of Google
The document discusses various SEO strategies and Google search technologies, focusing on patents related to search queries, ranking, and user experience. It highlights recent advancements in machine learning and language processing in relation to Google's search functionality and suggests ways to optimize search performance. Additionally, it touches on the importance of mobile optimization and user context in enhancing search visibility and engagement.
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Similar to Gianluca Fiorelli - The Alphabet of Google
Gianluca Fiorelli - The Alphabet of Google
- 2. GIANLUCA FIORELLI The Alphabetof Google @gfiorelli1
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- 4. A cosa pensaun SEO quando si parla di Google? gfiorelli1 #theinbounder
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- 11. gfiorelli1 #theinbounder I solitisospetti (nulla di personale, ma…)
- 12. Inside Search Blog OfficeHours Videos Dichiarazioni in rete Brevetti/Studi Acquisizioni Rumori Propaganda Fandom Nuovi Googlers
- 13. APP Indexing Rich Cards Realtime Indexing API Webmaster Blog Interstitial AMP Series Reviews in Local KGraph New AMP Tester More Https PWA Mobile 1st Rich Cards New Mobile Friendly Test API UGC Spam Crawl Budget Google Safe Browsing’s Site Status #Nohacked Similar Item How We Fought Webspam
- 14. Research Blog Più di 40 postsin 5 mesi e un solo grande tema gfiorelli1 #theinbounder
- 15. BrevettiSentence Compression Patent Associatingan 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/
- 16. Sentence Compression Patent Associatingan 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 Voice Search Informational Retrieval Local Search / Geolocalization Search User Experience Brevetti
- 17. Learning from UserInteractions 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 Studi - Information Retrieval
- 18. Learning from UserInteractions 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 Machine Learning Context Personal Search Search User Experience Studi - Information Retrieval
- 19. gfiorelli1 #theinbounder Generating Longand 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 Studi - Natural Language Processing
- 20. gfiorelli1 #theinbounder About 45Papers 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 Entity Search Context Rhetoric Personalization Search User Experience Studi - Natural Language Processing
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- 22. gfiorelli1 #theinbounder Immagini Video Video Influencers APPLink Sharing & Raccomandazioni locali & Geo- localizzazione AI Chatbots Ricerca Vocale & Linguaggio naturale ML & Datasets Entity Search Coding Collaborativo API
- 23. ProdottiGoogle Assistant DayDream VR GoogleHome GBoard Google Lens
- 25. Search User Experience SearchExperience Optimization SEO Tecnico HTTP/2 Mobile First AMP / PWA SEO come MARKETING Video & Immagini Ricerca Locale Semantic Search
- 26. UNDERSTANDING INFORMATION RETRIEVAL FILTERING &CLUSTERING RANKING PAINTING I N D E X I N G C R A W L I N G
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- 32. #1 Check Mobile Agent/ Client Handling
- 33. #2 Seguite il ModelloRAIL https://developers.google.com/web/fundamentals/performance/rail
- 34. #3 Implementare i datistrutturati per Rich Cards a Livello di Dominio e Pagina https://youtu.be/B0BA7Tswavs
- 35. A livello diPagina https://youtu.be/B0BA7Tswavs
- 36. A livello diDominio https://youtu.be/B0BA7Tswavs
- 37. #4 Prestare attenzione allagerarchia delle Urls > Hreflang!!
- 38. #5 Rivedete l’usabilità deicontenuti per evitare un Bounce Rate alto https://youtu.be/DIGfwUt53nI
- 39. #6 Rivedete la UXlungo tutto il Customer Journey facendo test su
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- 43. #1 Speed > HTTP/2 Multiplexedresources Browser to Server Prioritized by type & context Keep-Alive by default https://developers.google.com/web/fundamentals/performance/http2/
- 44. #2 Speed Front-End withImage Spriting
- 45. #3 Gestione dei tag- Use Tag Assistant extension for Chrome http://itseo.org/tagasstnt
- 46. #4 Usate JSON-LD peri Dati Strutturati https://moz.com/blog/using-google-tag-manager-to-dynamically-generate- schema-org-json-ld-tags
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- 54. Pensare la Localsearch solo come MyBusiness potrebbe limitare le opportunità che le aziende hanno di ottenere visibità e traffico organico.
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- 72. QUALITY MA UN MIXDI: • Coerenza tra pagina e intenzione di ricerca targetizzata
- 73. QUALITY MA UN MIXDI: • Capacità di offrire risposte alle domande esplicite ed esplicite
- 74. QUALITY MA UN MIXDI: • Facilità d’uso, soprattutto da Mobile
- 75. QUALITY MA UN MIXDI: • In un ambiente sicuro
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- 77. Uno degli eroidei miei figli
- 78. 1.The average agekids 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.
- 81. Similar items: Rich productsfeature on Google Image Search
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- 91. Generico (ma nontriviale) conoscimento di tutte le discipline del marketing online T E C H N I C A L M A R K E T E R S
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- 100. Buyer Persona KW Research Top 10 Ranking Sites *Query Intention Entity Recognition via CNL API Thesaurus Creation