Gianluca Fiorelli - The Alphabet of Google
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Gianluca Fiorelli ha parlato il 15 settembre al The Inbounder Milano, con una relazione dal titolo: "The Alphabet of Google".
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Gianluca Fiorelli - The Alphabet of Google
- 2. GIANLUCA FIORELLI The Alphabet of Google @gfiorelli1
- 4. A cosa pensa un SEO quando si parla di Google? gfiorelli1 #theinbounder
- 11. gfiorelli1 #theinbounder I soliti sospetti (nulla di personale, ma…)
- 12. Inside Search Blog Office Hours Videos Dichiarazioni in rete Brevetti/Studi Acquisizioni Rumori Propaganda Fandom Nuovi Googlers
- 13. APP Indexing Rich Cards Real time 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 posts in 5 mesi e un solo grande tema gfiorelli1 #theinbounder
- 15. BrevettiSentence 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/
- 16. 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 Voice Search Informational Retrieval Local Search / Geolocalization Search User Experience Brevetti
- 17. 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 Studi - Information Retrieval
- 18. 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 Machine Learning Context Personal Search Search User Experience Studi - Information Retrieval
- 19. gfiorelli1 #theinbounder 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 Studi - Natural Language Processing
- 20. 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 Entity Search Context Rhetoric Personalization Search User Experience Studi - Natural Language Processing
- 22. gfiorelli1 #theinbounder Immagini Video Video Influencers APP Link Sharing & Raccomandazioni locali & Geo- localizzazione AI Chatbots Ricerca Vocale & Linguaggio naturale ML & Datasets Entity Search Coding Collaborativo API
- 23. ProdottiGoogle Assistant DayDream VR Google Home GBoard Google Lens
- 25. Search User Experience Search Experience 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
- 29. MOBILE FIRST
- 32. #1 Check Mobile Agent / Client Handling
- 33. #2 Seguite il Modello RAIL https://developers.google.com/web/fundamentals/performance/rail
- 34. #3 Implementare i dati strutturati per Rich Cards a Livello di Dominio e Pagina https://youtu.be/B0BA7Tswavs
- 35. A livello di Pagina https://youtu.be/B0BA7Tswavs
- 36. A livello di Dominio https://youtu.be/B0BA7Tswavs
- 37. #4 Prestare attenzione alla gerarchia delle Urls > Hreflang!!
- 38. #5 Rivedete l’usabilità dei contenuti per evitare un Bounce Rate alto https://youtu.be/DIGfwUt53nI
- 39. #6 Rivedete la UX lungo tutto il Customer Journey facendo test su
- 40. SPEED
- 42. SPEEDCHECKLIST
- 43. #1 Speed > HTTP/2 Multiplexed resources Browser to Server Prioritized by type & context Keep-Alive by default https://developers.google.com/web/fundamentals/performance/http2/
- 44. #2 Speed Front-End with Image Spriting
- 45. #3 Gestione dei tag - Use Tag Assistant extension for Chrome http://itseo.org/tagasstnt
- 46. #4 Usate JSON-LD per i Dati Strutturati https://moz.com/blog/using-google-tag-manager-to-dynamically-generate- schema-org-json-ld-tags
- 47. #6 Service Workers - PWA
- 48. CONTESTO
- 51. CONTESTO
- 54. Pensare la Local search solo come MyBusiness potrebbe limitare le opportunità che le aziende hanno di ottenere visibità e traffico organico.
- 55. GBoard CONTESTO
- 56. CONTESTO
- 57. PARSING
- 58. SEMANTICA
- 59. @gfiorelli1
- 60. @gfiorelli1
- 64. @gfiorelli1
- 68. @gfiorelli1
- 69. @gfiorelli1
- 72. QUALITY MA UN MIX DI: • Coerenza tra pagina e intenzione di ricerca targetizzata
- 73. QUALITY MA UN MIX DI: • Capacità di offrire risposte alle domande esplicite ed esplicite
- 74. QUALITY MA UN MIX DI: • Facilità d’uso, soprattutto da Mobile
- 75. QUALITY MA UN MIX DI: • In un ambiente sicuro
- 76. Video & Immagini
- 77. Uno degli eroi dei miei figli
- 78. 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.
- 81. Similar items: Rich products feature on Google Image Search
- 82. Google Lens
- 91. Generico (ma non triviale) 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
- 100. Buyer Persona KW Research Top 10 Ranking Sites * Query Intention Entity Recognition via CNL API Thesaurus Creation