Company
LOGO
MovieMiner
A collaborative filtering system forpredicting Netflix user’s movie ratings
[ECS289G Data Mining]
Team Spelunker: Justin Becker, Philip Fisher-Ogden
The Problem
•Given a set of <movie, user, rating, date>entries, predict the ratings values for unknown<movie, user, ?, date> entries.
•Example:
–X-Men, Philip, 5, 05-02-2007
–Spiderman 3, Philip, 4, 05-10-2007
–X-Men, Justin, 4, 04-05-2006
–Spiderman 3, Justin, ?, 02-28-2008
•What rating do you predict Justin wouldgive Spiderman 3?
Our Approach - Motivation
•Motivating Factors
–Review current approaches taken by theNetflix prize top leaders
–Leverage and extend existing libraries, tominimize the ramp-up time required toimplement a working system
–Utilize the UC Davis elvis cluster to alleviateany scale problems
What - Our Approach
•Collaborative Filtering (CF)
–Weighted average of predictions from thefollowing recommenders:
•Slope One recommender
•Item-based recommender
•User-based recommender
What - Our Approach
•Leveraging three CF recommenders
–Similarities:
•Each uses prior preference information to predictvalues for unrated entries
–Differences:
•How is the similarity
between two entries
computed?
•How are the neighbors
selected?
•How are the interpolation weights determined?
Image source: http://taste.sourceforge.net/
Why - Our Approach
•Why Collaborative Filtering?
–“Those who agreed in the past tend to agreeagain in the future“
–Requires no external data sources
–Uses k-Nearest-Neighbor approaches topredict the class (rating) of an unknown entry
–Exists a full features CF Java library- Taste
–CF is one of two main approaches used bythe Netflix prize top leaders (with the otherbeing SVD).
How – Slope One Recommender
•Introduced by Daniel Lemire and AnnaMaclachlan
•Simple and accurate predictor
•Average difference between two items
•Weighted average to produce betterresults
•Number of user having rated both items
Ex: Slope One Recommender
Average difference between X-Men andSpiderman 3 is 1.
Justin's rating for Spiderman 3 is then 4+1=5
X-Men
Spiderman3
Batman Begins
Nacho Libre
Justin
4
?
5
4
Philip
5
3
4
2
Dan
4
4
5
5
Ian
3
4
3
3
Michael
2
3
1
5
How – User-based Recommender
•Predicts a user u’s rating for an item i:
–Find the k nearest neighbors to the user u
•Similarity measure = Pearson correlation
•Missing preferences are inferred by using theuser’s average rating
–Interpolate between those in-commonneighbors’ ratings for item i
•Interpolation weights = Pearson correlation
•Neighbors are ignored if they did not rate i
Ex: User-based Recommender
X-Men
Spiderman3
Batman Begins
Nacho Libre
avg
Justin
4
?
5
4
4.33333
Philip
5
3
4
2
3.5
Dan
4
4
5
5
4.5
Ian
3
4
3
3
3.25
Michael
2
3
1
5
2.75
centered data (user average)
X-Men
Spiderman 3
Batman Begins
Nacho Libre
EuclNorm
Justin
-0.3333
?
0.666666667
-0.333333333
0.816497
Philip
1.5
-0.5
0.5
-1.5
2.236068
Dan
-0.5
-0.5
0.5
0.5
1
Ian
-0.25
0.75
-0.25
-0.25
0.866025
Michael
-0.75
0.25
-1.75
2.25
2.95804
Ex: User-based Recommender
•Similarities are calculated using thePearson correlation coefficient (oncentered data):
•Interpolation between
nearest neighbors
produces the prediction:
User-user similarities
Justin-Phil
0.182574186
Justin-Dan
0.40824829
Justin-Ian
-3.14018E-16
Justin-Michael
-0.690065559
Prediction using 2-nearest neighbors
Philip, Dan
3.690983006
round(prediction)
4
How – Item-based Recommender
•Predicts a user u’s rating for an item i:
–Find the k most similar items to i
•Similarity measure = Pearson correlation
–Keep only similar items also rated by u
–Interpolate between the remaining items’ratings
•Interpolation weights = Pearson correlation
–Note: Item-item similarities allow for more efficient computations ascnt(items) << cnt(users) and, thus, the similarity matrix can be pre-computed and leveraged as needed.
Ex: Item-based Recommender
X-Men
Spiderman3
Batman Begins
Nacho Libre
Justin
4
?
5
4
Philip
5
3
4
2
Dan
4
4
5
5
Ian
3
4
3
3
Michael
2
3
1
5
avg
3.6
3.5
3.6
3.8
centered data (item average)
X-Men
Spiderman3
Batman Begins
Nacho Libre
Justin
0.4
?
1.4
0.2
Philip
1.4
-0.5
0.4
-1.8
Dan
0.4
0.5
1.4
1.2
Ian
-0.6
0.5
-0.6
-0.8
Michael
-1.6
-0.5
-2.6
1.2
Eucl Norm
2.24499
1
3.03973
2.6
Ex: Item-based Recommender
•Similarities are calculated using thePearson correlation coefficient (oncentered data):
•Interpolation between
nearest neighbors
produces the prediction:
Item-item similarities
S-Xm
0
S-BB
0.493463771
S-NL
0.192307692
Prediction from 2-nearestneighbors
BB, NL
4.719574665
round(prediction)
5
Initial Results
•Bottom line: correct=91934, loss=319,710
•Parameters used: 40% user, 60% item, 20nearest neighbors
•~97% scored with compositerecommender (user,item)
•~3% scored with random recommender
•RMSE 1.4445
Final Results
•Bottom line: correct=106,253,loss=236,523
•Parameters used: 25% user, 5% item,70% slope one, 20 nearest neighbors
•~97% scored with compositerecommender (user, item, slope one)
•~3% scored with weighted average
•RMSE: 1.0871
Questions?
?
Conclusion