4. Social Feed -> Web
Tweet Processing
Enrichment
User Analysis User Models
URL enrichment
Topic/Entity Enrichment
Entity/HashTag/Topic
Identification
Temporal Classification
Personalized
5. Challenge 1
• Design of User Model
[across million attributes and users]
• Design Similarity of Users
User Model
6. Challenge 2
• Design of Recommendation
[filtering across million attributes and users]
• Design ranking in user attributes [topic, entity,
hash tag ]
Recommendation
7. User Model -> Rec Engine
• Temporal Feature
• Profile Types
• Enrichment Types
8. Prototype – What we had
• Twitter 7.6 GB corpus
• Over 1.2 years
• 1218 users with avg.
of 175 tweets per user
• News corpus of 75,000+
articles
9. Prototype – What we Found
• Hash Tags distinct 100000+
• Topics - distinct 18 types
• Entities – distinct 39 types
Temporal Feature of Hash Tag
10. Prototype – our Play
• User Profiles
1)Hash Tag Profile
2)Entity Profile
3)Topic Profile
4)Experimental enriched
• Recommendation Engine
cosine similarity based
11. Results
• Hash tag profiles grow quickly and last for
period [Used Weka-Excel]
• Entity and Topic Profiles suggest relevant
suggestions
• Enrichment in Topics and Entity help to
correctly judge “subject”
<Improved Profile>
TEMPORAL EFFECT
PROFILE EFFECT
Enrichment EFFECT