Transcript Towards Implementing Better Movie Recommendation Systems

Towards Implementing Better Movie Recommendation Systems
• Volume of items available for sale increasing rapidly due to low barriers to
selling and distributing items online
Better Recommendation System  Targeted Advertising  Higher Sales
The Challenge: Every Customer is Different.
How do you ‘personalize’ recommendations
Rahul Thathoo, Zahid Khan
Comparison of Collaborative Filtering algorithms
on 90:10 MovieLens dataset
RMSE
1.06
1.04
1.02
1
0.98
0.96
0.94
0.92
0.9
Per User Avg
Slope 1
Slope 1 (W)
User-User (W)
Movie-Movie (LinReg)
Movie-Movie(Scaled)
Curse of Extreme Raters
• Some people only rate 1s/5s.
• We found ~600 users (10%) whose average rating > 4.25
• Need to adjust their ratings when evaluating predictions for them
1
0.9395
0.8434
0.8349
0.82356
0.70289
0.8
0.6
0.4
0.2
0
User-User
Pearson
Slope 1
Weighted
Movie-Movie Slope 1 Normal User Profile on
Correlation (W)
Movie Average
Drama / Romance
45/M
Children’s
Who would you turn to for
movie recommendations –
your dad or your friend??
Credibility of the user is determined by the set of movies
where the others agreed with him to the set of movies where
others disagreed.
1.06
1.04
1.02
1
0.98
0.96
0.94
0.92
0.9
0.88
0.86
0.84
Per User Avg
Slope 1
Slope 1 (W)
User-User (W)
Movie-Movie
(LinReg)
MovieMovie(Scaled)
User Profiling
User Credibility