1. Personalized
News
Recommendation
System with Different
User Modeling
Engineers:
Mahesh Attarde (201205685)
Arpita Raj Gupta (201101121)
Pankhuri Goyal (201101174)

2. Problem
INFORMATION OVERLOAD
Understand
“Me”
Recommend
news that
“Interest”
Me!
Use Social My
“Social
Behavior”
Learn Me over
“Time”

3. Problem
INFORMATION OVERLOAD
Understand
“Me”
Recommend
news that
“Interest”
Me!
Use Social My
“Social
Behavior”
Learn Me over
“Time”
Personalized News
Recommendation

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

12. Snap Shots

13. Snap Shots

14. Snap Shots

15. Snap Shots

16. Tools and Lib
• Weka
• Excel
• Open Cailais
• Stanford NPL lib
• Apache Mahout
• RDBMS -mysql

17. Thank You !
Professor Vasudev Varma
Aishvarya Singh
Guided By
Mentored By