1. @BCNPlaces4
@jordi_aranda @djkram
@techisthenewpop @dani_agent

2. @dani_agent

3. @dani_agent

4. What are we going to tell you?
location based
social
in-situ
recommender system
(e.g. Cicerone! Tell me a place for coffee now! // Go to Café Las Maravillas)@dani_agent

5. Location based
@dani_agent

6. If I click “Coffee”
1 Km.
Seriously
Foursquare?
@dani_agent

7. Social
@dani_agent

8. In Situ
@dani_agent

9. • My friends’ opinions
matter.
• Experts’s opinions matter
even more.
• Easy to query.
– Not another app!
Requirements
@dani_agent

10. Twitter as a Communication
Channel and Foursquare as a
Location Provider
41.371141, 2.144209
41.378676, 2.153479
@dani_agent

11. • Streaming API offers 1% of the total Twitter
Traffic.
– We never get the Over-Exceed alarm.
• Is the sample good enough?
– YES!
Is the Twitter Streaming Sample
Good Enough?
@dani_agent

12. Area Knowledge@dani_agent

13. Social Importance
ME
Stranger
@dani_agent

14. How does it work?
@dani_agent

15. Scalability Issues
@dani_agent

16. MongoDB
• Why?
– Raw tweets from Streaming API: JSON
– Geospatial Index
– Scalability
• When?
– Crawlers Writting
– Tweets and venues access by the algorithm.
@dani_agent
OVER
3.000.000
geo-tweets

17. Neo4J
• Why?
– Social Graph Representation.
– Java Native
• When?
– Insert relationships
– Querying on social graph
@dani_agent
OVER 200.000
nodes
and 1.5M
relationships

18. So… how good is Cicerone?
@dani_agent

19. Pilot Experiment
One of the options is calculated by
Cicerone and the other one by
Foursquare. But only Cicerone knows
which is which. TELL US WHICH ONE
YOU LIKE BETTER.@dani_agent

20. Pilot Results
• 20 users – 150 requests
@dani_agent

21. Conclusions
• Pre-production Geo-Social Recommender
System.
• Spatial Capabilities and JSON by MongoDB
• Social Relationships by Neo4j.
• Good experimental results.
@dani_agent

22. @dani_agent
@dani_agent