6. Current Best Approach: Collaborative Filtering with Temporal Dynamics: Time or history of users are Important because: People may changes their behavior or their preferences during their life. Capturing time drifting patterns in user behavior is essential to improving accuracy of recommenders. customer preferences change as new products and services become available
7. How Deep we should Consider in Time Time (history of user) Past Now
8. Current Best Approach: Collaborative Filtering with Temporal Dynamics: Overall Rating: ? 1 2 3 4 What is next!?
12. 5 Star Rating Global Rating: Local Rating: Some users just rate between 2-4 Some just 4,5 5 Star rating is the most unreliable rating feature!
13. Rating for What?! It’s Interesting! That movie is Excitement! I’m Like that movie! You may Interest in one movie, but after you watched it, you may don’t like that! 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.
18. New Approach: Local Rating: 1 2 3 4 Class: A Class: B Class: A Class: C Find Similarities 1 2 3 4 1 2 3 4
19. New Approach: User: Each user is individually monitor and then base on their behavior/interests group them in clusters. Consider User Rating as Local and for overall rating convert local rating to global Movies: Each movie has a global ratingand Class(Static and Dynamic)
26. Support!!!!? 2,910,000 112 blog 11,600,000 2,840 blog 17,900,000 1,680 blog 46,100,000 7,040 7,940,000 pages 1,010 blog 1 5 2 3 4
27. New Approach: ? Time is: SAT Night Watch these: 1 3 You may like that because: Director: James Cameron Story: Drama You may enjoy that because: Movie Support is : very high User Support is : very high
