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HTML 5 Development for Windows Phone and DesktopDoris Chen
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User Engagement Teardown: Which Coffee Is The Cream Of The Crop?Iterable
This is an Iterable User Engagement Teardown - focusing on, and comparing, Peet's Coffee & Tea, Philz Coffee and Starbucks.
It analyzes how these companies do user engagement in the first 3 weeks post-signup. This teardown also incorporates social media and engagement. The User Engagement Timeline lets you visualize all engagement, and we evaluate individual emails/push notifications and suggest improvements.
As always, we’ve identified things these companies do well and some areas where there is room for improvement. Everything shown in the slides (and any recommendations) can be implemented with Iterable.
We hope you enjoy this teardown as much as you enjoy the coffee from these three great companies!
Semantic user profiling and Personalised filtering of the Twitter streamFabrizio Orlandi
Presentation at Kno.e.sis - Feb 2012.
The presentation describe my current PhD research at DERI and the work done in 5 weeks during a collaboration in Kno.e.sis with Pavan Kapanipathi, Prof. Amit Sheth, Prof. T. K. Prasad and the rest of the group.
- video: http://youtu.be/MmF5HxIVUwA
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User Engagement Teardown: Which Coffee Is The Cream Of The Crop?Iterable
This is an Iterable User Engagement Teardown - focusing on, and comparing, Peet's Coffee & Tea, Philz Coffee and Starbucks.
It analyzes how these companies do user engagement in the first 3 weeks post-signup. This teardown also incorporates social media and engagement. The User Engagement Timeline lets you visualize all engagement, and we evaluate individual emails/push notifications and suggest improvements.
As always, we’ve identified things these companies do well and some areas where there is room for improvement. Everything shown in the slides (and any recommendations) can be implemented with Iterable.
We hope you enjoy this teardown as much as you enjoy the coffee from these three great companies!
Semantic user profiling and Personalised filtering of the Twitter streamFabrizio Orlandi
Presentation at Kno.e.sis - Feb 2012.
The presentation describe my current PhD research at DERI and the work done in 5 weeks during a collaboration in Kno.e.sis with Pavan Kapanipathi, Prof. Amit Sheth, Prof. T. K. Prasad and the rest of the group.
- video: http://youtu.be/MmF5HxIVUwA
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Improving Natural Language Inference Using External Knowledge in the Science ...Pavan Kapanipathi
Natural Language Inference (NLI) is fundamental to many Natural Language Processing (NLP) applications including semantic search and question answering. The NLI problem has gained significant attention due to the release of large scale, challenging datasets. Present approaches to the problem largely focus on learning-based methods that use only textual information in order to classify whether a given premise entails, contradicts, or is neutral with respect to a given hypothesis. Surprisingly, the use of methods based on structured knowledge – a central topic in artificial intelligence – has not received much attention vis-a-vis the NLI problem. While there are many open knowledge bases that contain various types of reasoning information, their use for NLI has not
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Social media has experienced immense growth in recent times. These platforms are becoming increasingly common for information seeking and consumption, and as part of its growing popularity, information overload pose a significant challenge to users. For instance, Twitter alone generates around 500 million tweets per day and it is impractical for users to have to parse through such an enormous stream to find information that are interesting to them. This situation necessitates efficient personalized filtering mechanisms for users to consume relevant, interesting information from social media.
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Personalized Filtering of Twitter Stream
1. Personalized Filtering of
the Twitter Stream
Pavan Kapanipathi 1,2, Fabrizio Orlandi1,
Amit Sheth2 ,Alexandre Passant 1
1 Digital Enterprise Research Institute, Galway – Ireland
2 Kno.e.sis, Dayton, OH- USA
1
2. Motivation
Twitter – Growth
Information Overload
http://www.cmswire.com/cms/customer-experience/35-key-twitter-statistics-infographic-012384.php
2
3. Motivation
• How many people should I follow ?
• Am I receiving latest/complete information ?
3
4. Background
Twarql – Streaming annotated tweets
Semantic Web Technologies
Annotate Tweets (DBpedia Entities)
Filter Stream using SPARQL Queries formulated
Example:
Stream all the tweets related to Semantic Web generated in
Germany
?tweet moat:taggedWith ?topic .
?topic dcterms:subject category:Semantic_Web .
?tweet sioc:has_creator ?user .
?user geonames:locatedIn dbpedia:Germany .
4
5. Approach -- Overview
The new
iPhone has a Broadcast
3.5-inch screen,
Football
released today
User
Profiles
Filter
Apple
5
6. Annotate: iPhone Get
?user foaf:interest Subscribers
The new
iPhone has a 3.5-
inch screen,
Architecture
dbPedia:iPhone
Union
based on
preference
?user foaf:interest
released today
Category:Apple
Get Interested
Subscribers
RDF
Semantic Filter Notify Update
A
N RDF
N Store and
O
T
Query Topics Semantic Hub
A Fetch Updates
T RS
O
R S Store FOAF
Update RSS
Profile Generator
Push Updates
to Interested
Users
Create Profile
6
7. Contribution
Profile Generator
Automatic generation of User Profiles
Semantic Filter
Annotating Twitter Stream with concepts from Linked
Open Data
Semantic Hub
Delivering tweets to appropriate Interested Users (near
real-time)
7
8. Profile Generator
Get Interested
Subscribers
RDF
Semantic Filter Notify Update
A
N RDF
N Store and
O
T
Query Topics Semantic Hub
A Fetch Updates
T RS
O
R S Store FOAF
Update RSS
Profile Generator
Create Profile
8
9. Profile Generator
Disconnected
Social websites
Isolated data
silos
Social Networking Sites as Walled Gardens by David Simonds (Used with permission)
9
10. Interlink social websites
Integration
& Merge and model user data
User Modelling
User Profile
Personalise users’ experience
using their profile
Recommendations Adaptive Systems
Search Personalisation
10
11. Profile Generator
Data Extraction
Twitter, Facebook, LinkedIn
Example: Tweets, FB Likes
Profile Generation
Interests extracted from collected data
Entity spotting (user generated data)
Explicit interests specified by user (Facebook likes etc)
Weighted Interests
Semantic Representation of Profiles
FOAF profile
11
12. Semantic Filter
Get Interested
Subscribers
RDF
Semantic Filter Notify Update
A
N RDF
N Store and
O
T
Query Topics Semantic Hub
A Fetch Updates
T RS
O
R S Store FOAF
Update RSS
Profile Generator
Create Profile
12
16. Semantic Hub
Get Interested
Subscribers
RDF
Semantic Filter Notify Update
A
N RDF
N Store and
O
T
Query Topics Semantic Hub
A Fetch Updates
T RS
O
R S Store FOAF
Update RSS
Profile Generator
Create Profile
16
17. PubSubHubbub
Protocol
PubSubHubbub is an extension to RSS/Atom
Open, web hook based, pubsub protocol for Real-time notification
of updates
Drawback
Publisher has no control over the dissemination of his content
Extension – Semantic Hub
Publisher controlled dissemination
SPARQL Query representing the subset of target subscribers
17
18. PubSubHubbub Protocol
Extension
Hey I have new Here is the
Give me new content
content for feed the new
X + my of feed X
content Sub - A
preference Y
Sub - B
Pub Semantic Hub
Sub - C
Here it Sub - D
is
Get the subscribers
Social of Pub whose profile
Graph matches preference
Y
18
19. Semantic Hub
RSS Extension
Preference – to include the sparql queries
Push content
FOAF profiles of the subscribers are matched with the
preference
Interested subscribers receive the content
Accepted as a full paper in the In-Use track at ISWC 2011
19
20. Conclusion
Single consistent profile rather than profiles on multiple social networks
User Profile Generation
Architecture for Personalization of twitter stream
Reduce load on users to follow others
Public tweets streamed
Access to information from experts in domains
Are you following experts in your domain of interest?
Experts public tweets will be streamed
Dynamic groups of users
Interest Driven
20
21. Future work -- Why RDF
Twarql features
Concept feeds as interests of the users
22. Future Work
Periodic FOAF profile generation for users
Twitter Stream reflecting the changing interests
Extending to other social networks (G+, FB)
22
23. Thanks
Contact us on Twitter
@pavankaps
@badmotorf
@terraces
@amit_p
Email: {pavan, amit}@knoesis.org
{fabrizio.orlandi, alexandre.passant}@deri.org
This work is funded by (1) Science Foundation Ireland under grant number SFI/08/CE/I1380 (Lıon 2) and by an
IRCSET scholarship supported by Cisco Systems (2) Social Media Enhanced Organizational Sensemaking in
Emergency Response, National Science Foundation under award IIS-1111182, 09/01/2011 - 08/31/2014.
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27. Weighing function based on RTs and other active
engagements of the user
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Editor's Notes
How can both of these be done at one – Personalizing your twitter streamPut the name of the author of the source
Friends, industry experts and favourite celebrities
User generated content from the social networks (not profiles from the social network wont include the social graph)----- Meeting Notes (10/19/11 15:25) -----Rather than SPARQL syntax -- Generic (Make sure that the keywords are highlighted)
----- Meeting Notes (10/19/11 15:25) -----Rather than profile genrator (Aggregating profile information)----- Meeting Notes (10/19/11 15:27) -----Before contributions give some background
User generated content from the social networks (not profiles from the social network wont include the social graph)----- Meeting Notes (10/19/11 15:25) -----Rather than SPARQL syntax -- Generic (Make sure that the keywords are highlighted)
----- Meeting Notes (10/19/11 15:25) -----1. Emphasize on Filtering2. Twarql enabled data
User generated content from the social networks (not profiles from the social network wont include the social graph)----- Meeting Notes (10/19/11 15:25) -----Rather than SPARQL syntax -- Generic (Make sure that the keywords are highlighted)
Highlight when speaking about the particulars
User generated content from the social networks (not profiles from the social network wont include the social graph)----- Meeting Notes (10/19/11 15:25) -----Rather than SPARQL syntax -- Generic (Make sure that the keywords are highlighted)
Merge advantages and conclusion slide
Alex Blog post about.We use the semantic web technologies like RDF and SPARQL to filter the data. The information in the tweets is extracted and then the RDF triples are generated for each tweet. SPARQL queries are used to query these triples.For example. A sprarql Query which queries for all the tweets which has entities related to the dbPedia:HelathCare is subscribed. Our system filters the incoming data with this query and outputs the tweets.Pu