CSTalks - Real movie recommendation - 9 Mar

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CSTalks - Real movie recommendation - 9 Mar

  1. 1. Real Movie Recommendation<br />By Ghasem Heyrani-Nobari<br />
  2. 2.
  3. 3. TOP 10 cnet.com<br />1. Jinni <br />2. Taste Kid<br />3. Nanocrowd<br />4. Clerkdogs <br />5. Criticker<br />6. IMDb <br />7. Flixster<br />8. Movielens<br />9. Rotten Tomatoes<br />10. Netflix<br />
  4. 4. jinni.com<br />Search base on your:<br />Mood<br />Time available<br />Reviews <br />Movie features<br />Your movie history<br />Semantic search<br />
  5. 5. Which one you Choose and WHY?!<br />
  6. 6. Current Best Approach:<br />Collaborative Filtering with Temporal Dynamics:<br />Time or history of users are Important because:<br />People may changes their behavior or their preferences during their life.<br />Capturing time drifting patterns in user behavior is essential to improving accuracy of recommenders.<br />customer preferences change as new products and services become available<br />
  7. 7. How Deep we should Consider in Time<br />Time (history of user)<br />Past<br />Now<br />
  8. 8. Current Best Approach:<br />Collaborative Filtering with Temporal Dynamics:<br />Overall Rating:<br />?<br />1<br />2<br />3<br />4<br />What is next!?<br />
  9. 9. My Approach<br />
  10. 10. 5 Star Rating!<br />
  11. 11.
  12. 12. 5 Star Rating<br />Global Rating:<br />Local Rating: <br />Some users just rate between 2-4<br />Some just 4,5<br />5 Star rating is the most unreliable rating feature!<br />
  13. 13. Rating for What?!<br />It’s Interesting!<br />That movie is Excitement!<br />I’m Like that movie!<br />You may Interest in one movie, but after you watched it, you may don’t like that!<br />or before you watched a movie you may know that you don’t like it, but because you have some interest in that movie you watched that.<br />
  14. 14. Just Click!<br />
  15. 15. Movie Features?<br />Cast<br />Actor<br />Director<br /> Genre<br />What else are missing?<br />
  16. 16. Movie Class?<br />Dynamic Features:<br />Supports<br />
  17. 17. Movies Supports?<br />Supports Factors:<br />
  18. 18. New Approach:<br />Local Rating:<br />1<br />2<br />3<br />4<br />Class: A<br />Class: B<br />Class: A<br />Class: C<br />Find Similarities<br />1<br />2<br />3<br />4<br />1<br />2<br />3<br />4<br />
  19. 19. New Approach:<br />User: <br />Each user is individually monitor and then base on their behavior/interests group them in clusters.<br />Consider User Rating as Local and for overall rating convert local rating to global<br />Movies:<br />Each movie has a global ratingand<br />Class(Static and Dynamic)<br />
  20. 20. Model:<br />User Monitor<br />Patterns of: <br /><ul><li>Individual Users
  21. 21. Users-Users
  22. 22. Users-Movies
  23. 23. Movies-Movies
  24. 24. Overall Users and Movies over time</li></ul>1<br />Movie Monitor<br />4<br />3<br />2<br />Recommend<br />&<br />predict<br />Clusters of: <br /><ul><li>Users
  25. 25. Movies</li></ul>Update<br />
  26. 26. Support!!!!?<br />2,910,000 <br />112 blog<br />11,600,000<br />2,840 blog<br />17,900,000 <br />1,680 blog<br />46,100,000<br />7,040<br />7,940,000 pages <br />1,010 blog<br />1<br />5<br />2<br />3<br />4<br />
  27. 27. New Approach:<br />?<br />Time is: SAT Night<br />Watch these:<br />1<br />3<br />You may like that because: <br />Director:  James Cameron<br />Story: Drama<br />You may enjoy that because:<br />Movie Support is : very high<br />User Support is : very high<br />
  28. 28. Thanks ;)<br />

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