MovieIt Presentation for dragon's den pitch

1. Foroogh Sedighi Evangelos Tolias Hamid Sar Koysina Kristina

2. Proposed system What movies would I like to watch? Recommendation scenarios What cinema is the bestplace to watch this movie? What movie is the best optionto watch in this Cinema?

3. Related works Academia Market Jinni Taste Engine Movies Now Filxup Faves for Facebook Trust-based recommendations Recommendation Systems Advanced Recommendation algorithms

4. See the difference

5. Social Aspects of MovieIt

6. Economic Aspects of MovieIt

7. User Value Fulfillment of self expression Captures a movie social activity aspect Better recommendations based on: Geolocation information Trusted Network (movies that friend that I follow watched and rated) Movies I saw and rated

8. Revenue Models Supported Affiliate Model gain traffic from social networks Targeted Advertisement exploiting our recommendation system Revenue

9. The Long Tail Recommendations for Popular movies Cult Movies (Art house films) SOURCE: www.searchengineguide.com

10. Further development Future work Folksonomies(users add keywords to movies for better search) Streamed twitter feeds

11. System Architecture Twitter Rest API (OAuth v1.oa) Facebook Graph API (Oauth 2.0) Themoviedb Rest API MySql (JDBC) JSON-simple Xstream Scribe-Java Pure HTML5 JavaScript Jquery Jquery Mobile Native mobile apps Mobile Web apps Web apps Desktop apps Clients Rest/JSON Rest/JSON HTML MovieIt Rest/JSON Tomcat Apache HTTP Server MovieIt Servlet Rest/JSON TmDB Local DB MySQL SOURCE iPhone:www.clker.com, SOURCE Computer: www.manonatha.net

12. Recommendations Multidimensional recommendations What to watch? Where to watch? Should I watch it? Who wants to watch it with me? SOURCE: http://clowder.net/hop/Riemann/Riemann.html

13. Conclusion Meaningful information MovieIt Network effect Infrastructure

14. Thank You! Any Questions?