Breve guida all'utilizzo di Twitter: il tweet perfetto, come interagire con i contenuti, come interagire con le persone, come aumentare il numero di follower, l'etichetta di Twitter
Su web le immagini catturano lattenzione, ma solo le parole permettono di approfondire un argomento, aggiungendo alle emozioni anche dati e informazioni. Per questo la scrittura 竪 unabilit che deve essere affinata da chiunque desideri utilizzare in modo professionale i social media.
Yelp Data Challenge - Discovering Latent Factors using Ratings and ReviewsTharindu Mathew
油
A restaurant's average rating and reviews on Yelp in influence customers to an incredible degree. An extra half-star rating causes restaurants to sell out 19 percentage points (49%) more frequently. Despite the impact on the restaurant's business, achieving a better overall rating is not straightforward. A user may give only one star to the restaurant just because he or she found the quality of service to be abysmal even though the food and the restaurant's location were up to his or her standard. These facts may have been mentioned in the review in detail but the final rating would just reflect the poor quality of service. The user rating alone does not provide any additional details, and as a result, the restaurant may not be able to understand which aspects create a negative impact on user experience. Another case may be that a certain popular dish will make users give the restaurant five star ratings, but they would not be satisfied with another aspect of the restaurant such as the dessert. The high user ratings may hide the fact that some aspects of the user experience was negative and that the restaurant has room to improve. Traditional recommender systems usually use only the aggregated ratings without considering the hidden factors in the preference of the users and the properties of the restaurants. For the restaurant domain, this could mean main cuisine, dessert, service, staff friendliness, knowledge of staff, location, ambiance, price and many more aspects. Without considering the ratings for individual aspects, it is likely that the recommendation systems will give inaccurate predictions to restaurants as well as users.
In this project, we aim to uncover hidden details about the users' preferences with respect to restaurant properties. With this information, we can provide precise recommendations to the restaurants regarding what aspects they should concentrate on to improve user experience. Since we are backed by more meaningful information about users' preferences we can provide better recommendations to users as to which restaurants they would prefer and why. To summarize, from the results of this project, we can answer the following questions: "what does a particular user care about when dining from a restaurant?", "which aspect should the restaurant improve in order to effectively increase the rating?", and "which restaurant is the best for a particular user?"
This document discusses enabling a data-driven agile business through data analytics and summarizes WSO2's progress in building products to support big data strategies. It notes that WSO2 released new versions of its BAM and CEP products in 2012 to focus on performance, scalability, and customizability. The document outlines WSO2's vision for 2013 of fusing its products to enable "Data to knowledge" and ambitions to process 10 terabytes of data in 2 seconds. It also notes that the amount of data created and shared globally is expected to grow dramatically between 2012 and 2015.
The document describes 3DRSim, a 3D reconstruction simulator that uses a single image to generate 3D reconstructions of complex objects. It explores using multiple projectors and cameras with algorithms that could be applied to real world scenarios. The demo shows the simulator in action. Challenges include coming up with a pattern and algorithm to get correspondence from one image. The approach detects edges between images, corresponds each edge, and performs ray intersections. Future work includes allowing different camera configurations and more robust methods.
A Robot powered by a Raspberry Pi, is built and used as a dynamic moving projector for Appearance Editing work.
This was developed at the CGVLab at Purdue University for ongoing Appearance Editing research.
Combining PID controllers with Robot Motion PlanningTharindu Mathew
油
A presentation that explores the possibility of the Elastic Band construct, which combines controllers with motion planning for more dynamic robot motion
The feedback cycle in the context of APIs talks about gathering API data, slice and dicing this data to gather information, deciding any actions and tuning API parameters. There are considerations for each step, and these actions can easily be implemented through WSO2 BAM
The document discusses WSO2's Business Activity Monitor (BAM) product for data analytics. It provides insights from raw data through features like data connectors, query functions, dashboards, reports, and distributed deployment. BAM supports big data through capabilities like big data storage, analytics and speed. It also allows federated analytics across multiple data sources.
Lezione frontale di Social Media Management destinata sopratutto a chi parte da zero e si appresta in maniera autodidatta a gestire o gestisce i profili social di un piccolo brand o di una attivit propria.
Come creare e gestire un Account Twitter finalizzato al Business; Do's & Dont's, best practices & common mistakes. Quali tool utilizzare per gestire la scalabilit dello strumento.
Introduzione a Twitter per l'uso in ambito turistico alberghiero: caratteristiche, glossario, casi studio e strumenti per l'analisi del social network dai messaggi da 140 caratteri.
Yelp Data Challenge - Discovering Latent Factors using Ratings and ReviewsTharindu Mathew
油
A restaurant's average rating and reviews on Yelp in influence customers to an incredible degree. An extra half-star rating causes restaurants to sell out 19 percentage points (49%) more frequently. Despite the impact on the restaurant's business, achieving a better overall rating is not straightforward. A user may give only one star to the restaurant just because he or she found the quality of service to be abysmal even though the food and the restaurant's location were up to his or her standard. These facts may have been mentioned in the review in detail but the final rating would just reflect the poor quality of service. The user rating alone does not provide any additional details, and as a result, the restaurant may not be able to understand which aspects create a negative impact on user experience. Another case may be that a certain popular dish will make users give the restaurant five star ratings, but they would not be satisfied with another aspect of the restaurant such as the dessert. The high user ratings may hide the fact that some aspects of the user experience was negative and that the restaurant has room to improve. Traditional recommender systems usually use only the aggregated ratings without considering the hidden factors in the preference of the users and the properties of the restaurants. For the restaurant domain, this could mean main cuisine, dessert, service, staff friendliness, knowledge of staff, location, ambiance, price and many more aspects. Without considering the ratings for individual aspects, it is likely that the recommendation systems will give inaccurate predictions to restaurants as well as users.
In this project, we aim to uncover hidden details about the users' preferences with respect to restaurant properties. With this information, we can provide precise recommendations to the restaurants regarding what aspects they should concentrate on to improve user experience. Since we are backed by more meaningful information about users' preferences we can provide better recommendations to users as to which restaurants they would prefer and why. To summarize, from the results of this project, we can answer the following questions: "what does a particular user care about when dining from a restaurant?", "which aspect should the restaurant improve in order to effectively increase the rating?", and "which restaurant is the best for a particular user?"
This document discusses enabling a data-driven agile business through data analytics and summarizes WSO2's progress in building products to support big data strategies. It notes that WSO2 released new versions of its BAM and CEP products in 2012 to focus on performance, scalability, and customizability. The document outlines WSO2's vision for 2013 of fusing its products to enable "Data to knowledge" and ambitions to process 10 terabytes of data in 2 seconds. It also notes that the amount of data created and shared globally is expected to grow dramatically between 2012 and 2015.
The document describes 3DRSim, a 3D reconstruction simulator that uses a single image to generate 3D reconstructions of complex objects. It explores using multiple projectors and cameras with algorithms that could be applied to real world scenarios. The demo shows the simulator in action. Challenges include coming up with a pattern and algorithm to get correspondence from one image. The approach detects edges between images, corresponds each edge, and performs ray intersections. Future work includes allowing different camera configurations and more robust methods.
A Robot powered by a Raspberry Pi, is built and used as a dynamic moving projector for Appearance Editing work.
This was developed at the CGVLab at Purdue University for ongoing Appearance Editing research.
Combining PID controllers with Robot Motion PlanningTharindu Mathew
油
A presentation that explores the possibility of the Elastic Band construct, which combines controllers with motion planning for more dynamic robot motion
The feedback cycle in the context of APIs talks about gathering API data, slice and dicing this data to gather information, deciding any actions and tuning API parameters. There are considerations for each step, and these actions can easily be implemented through WSO2 BAM
The document discusses WSO2's Business Activity Monitor (BAM) product for data analytics. It provides insights from raw data through features like data connectors, query functions, dashboards, reports, and distributed deployment. BAM supports big data through capabilities like big data storage, analytics and speed. It also allows federated analytics across multiple data sources.
Lezione frontale di Social Media Management destinata sopratutto a chi parte da zero e si appresta in maniera autodidatta a gestire o gestisce i profili social di un piccolo brand o di una attivit propria.
Come creare e gestire un Account Twitter finalizzato al Business; Do's & Dont's, best practices & common mistakes. Quali tool utilizzare per gestire la scalabilit dello strumento.
Introduzione a Twitter per l'uso in ambito turistico alberghiero: caratteristiche, glossario, casi studio e strumenti per l'analisi del social network dai messaggi da 140 caratteri.
Come creare una presenza efficace su Twitter; i passi per creare una lista di follower su Twitter.際際滷 presentate da Leonardo Bellini al Master in Social Media marketing dello IULM
Un modo nuovo di proporre l'azienda all'interno di nuovi canali di comunicazione. Un potente strumento di marketing e di gestione della reputazione on line.
Una presentazione per iniziare a scoprire cos'竪 e come funziona Twitter: pensata per chi non l'ha mai usato e vuole imparare a cinguettare in pochi minuti!
Twitter for Business- 3属parte della miniserie dedicata a Twitter:
Quali sono gli obiettivi di business associabili a Twitter? Come pu嘆 essere utilizzato per il business?
Come posso promuovere i miei tweet o il mio account usando gli strumenti di adv. di Twitter?
Quali sono le metriche e con quali strumenti di Twitter analytcs posso misurare le performance?
Tutto quello che occorre per partire su Twitter; dalla creazione di un profilo professionale, a come si scrivono tweet per creare una massa organica di follower: ecco l'indice:
Creare un Profilo Twitter
Larte di twittare
Twitter Policy
Come raggiungere una massadi follower
Come utilizzare alcuni strumenti automatici
Come farsi seguire
4. 1) Tieni aggiornati i tuoi follower.
Quando partecipi a un evento, ad esempio,
posta foto, video e informazioni che possono
coinvolgere gli altri utenti.
5. 2) Informa la tua rete.
Condividi dati, ricerche, statistiche, scoperte ed
eventi interessanti, informazioni utili, ecc.
I tuoi follower ti ringrazieranno.
E aumenteranno.
6. 3) Partecipa.
Segui i profili di altri utenti che trovi interessanti
per i tweet che pubblicano.
Rispondi a domande o altri post.
Fai domande, entra nelle discussioni e di la tua.
7. 4) Utilizza il motore di ricerca di Twitter.
(search.twitter.com).
Troverai informazioni su di te, sul tuo brand, sui
tuoi prodotti o servizi (o su quelli dei competitor).
8. 5) Con Twitter puoi avvisare di offerte.
Puoi utilizzare Twitter anche per avvisare
clienti e follower di offerte speciali. Gli utenti
riceveranno queste informazioni in tempo reale.
9. 6) Prova TweetDeck o HootSuite.
Sono applicazioni gratuite, utili per conoscere
subito quando nei tweet viene menzionato
qualcosa per te importante (come il tuo nome o il
tuo prodotto, ad esempio).
11. 1) Non inserire troppe infromazioni
in un solo tweet. Ogni parola deve
essere importante per il messaggio.
12. N.B.
Un tweet di 85/100 caratteri lascia lo
spazio per il testo di un retweet.
13. 2) Punta alla semplicit e alla
chiarezza (evita abbreviazioni).
Utilizza link ad articoli completi.
Accorcia i link con bit.ly.Accorcia i link con bit.ly.
14. 3) Utilizza parole chiave e hashtag:
fornisci un contesto ai tuoi lettori.
15. 4) Quando scrivi un tweet, riporta
subito linformazione pi湛 rilevante e
poi linka larticolo.
16. 5) Scrivi titoli efficaci, che attirino
lattenzione del lettore verso la notizia,
il post, limmagine, la promozione, ecc.
17. 6) Grafici, immagini, infografiche:
i contenuti grafici arricchiscono i
contenuti e invitano allazione.