Download - 2lu: Movie Recommendations for two!
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Allen Sussman
Find movies for two
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Can we find a movie we’ll both actually like?
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Each person enters movies they like and 2lu finds movies they’ll both like
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Each person enters movies they like and 2lu finds movies they’ll both like
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Movies->Clue Kids Jaws Babe Big
1 5 3 4 5 None2 None 5 1 5 None3 3 None 1 4 34 1 5 1 4 35 2 4 1 4 5
Users->
Ratings Table from Algorithm: Collaborative Filtering
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Movies->Clue Kids Jaws Babe Big
1 5 3 4 5 None2 None 5 1 5 None3 3 None 1 4 34 1 5 1 4 35 2 4 1 4 5
Users->
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Movies Similarity MatrixAlgorithm: Collaborative Filtering
Ratings Table from
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Movies->Clue Kids Jaws Babe Big
1 5 3 4 5 None2 None 5 1 5 None3 3 None 1 4 34 1 5 1 4 35 2 4 1 4 5
Users->
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Movies Similarity Matrix
Say User 1 likes Clue User 2 likes Babe
Algorithm: Collaborative FilteringRatings Table from
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Movies->Clue Kids Jaws Babe Big
1 5 3 4 5 None2 None 5 1 5 None3 3 None 1 4 34 1 5 1 4 35 2 4 1 4 5
Users->
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Movies Similarity Matrix
Say User 1 likes Clue User 2 likes Babe
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Algorithm: Collaborative Filtering
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Ratings Table from
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Movies->Clue Kids Jaws Babe Big
1 5 3 4 5 None2 None 5 1 5 None3 3 None 1 4 34 1 5 1 4 35 2 4 1 4 5
Users->
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Movies Similarity Matrix
Say User 1 likes Clue User 2 likes Babe
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Clue1
0.20.30.40.5
Babe0.40.20.21
0.5
f( , )=
Algorithm: Collaborative Filtering
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Ratings Table from
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Movies->Clue Kids Jaws Babe Big
1 5 3 4 5 None2 None 5 1 5 None3 3 None 1 4 34 1 5 1 4 35 2 4 1 4 5
Users->
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Movies Similarity Matrix
Say User 1 likes Clue User 2 likes Babe
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Clue1
0.20.30.40.5
Babe0.40.20.21
0.5
f( , )=
Algorithm: Collaborative Filtering
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Clue 0.6Kids 0.2Jaws 0.225Babe 0.6Big 0.5
Ratings Table from
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Movies->Clue Kids Jaws Babe Big
1 5 3 4 5 None2 None 5 1 5 None3 3 None 1 4 34 1 5 1 4 35 2 4 1 4 5
Users->
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Movies Similarity Matrix
Say User 1 likes Clue User 2 likes Babe
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Clue1
0.20.30.40.5
Babe0.40.20.21
0.5
f( , )=
Algorithm: Collaborative Filtering
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Clue 0.6Kids 0.2Jaws 0.225Babe 0.6Big 0.5
Largest number is for the movie Big. Users should watch it!
Ratings Table from
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Movies->Clue Kids Jaws Babe Big
1 5 3 4 5 None2 None 5 1 5 None3 3 None 1 4 34 1 5 1 4 35 2 4 1 4 5
Users->
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Movies Similarity Matrix
Say User 1 likes Clue User 2 likes Babe
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Clue1
0.20.30.40.5
Babe0.40.20.21
0.5
f( , )=
Algorithm: Collaborative Filtering
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Clue 0.6Kids 0.2Jaws 0.225Babe 0.6Big 0.5
Largest number is for the movie Big. Users should watch it!
Cross-validation: 90% of recommendations were given a rating of 3 or above by both users
Ratings Table from
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Allen Sussman, Ph.D.
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GoR(movie)=mean(s1,s2) - 0.25diff(s1,s2)
Similarity of movie to user 1
movie
Similarity of movie to user 2
movie
Goodness-of-recommendation (GoR) function
If average similarity to the input movies is high, that’s good!
If movie is very similar to user 1 movie but not
user 2 movie (or vice versa), that’s
bad!
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0.60.2
0.2250.60.5
Movies->Clue Kids Jaws Babe Big
1 5 3 4 5 None2 None 5 1 5 None3 3 None 1 4 34 1 5 1 4 35 2 4 1 4 5
Users->
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Movies Similarity Matrix
Say User 1 likes Clue User 2 likes Babe
Clue Kids Jaws Babe BigClue 1 0.2 0.3 0.4 0.5Kids 0.2 1 0.3 0.2 0.3Jaws 0.3 0.3 1 0.2 0.3Babe 0.4 0.2 0.2 1 0.5Big 0.5 0.3 0.3 0.5 1
Clue1
0.20.30.40.5
Babe0.40.20.21
0.5
f( , )= 0.60.2
0.2250.60.5
Largest number is for the movie Big. Users should watch it!f(s1,s2)=mean(s1,s2)-α*diff(s1,s2)
f(s1,1,s1,2,…,s2,1,s2,2,…) = mean(s1,1,s1,2,…,s2,1,s2,2,…)- α*std(s1,1,s1,2,…,s2,1,s2,2,…)-β*diff(mean(s1,1,s1,2,…),mean(s2,1,s2,2,…))
For multiple input movies,
Ratings TableAlgorithm
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Training Set
Test Set
Users->
Movies->Ratings Table
Cross-ValidationMovies Similarity Matrix
Test Set Features Ground Truth
Consider two users in test set
User 1 User 2
Use algorithm and similarity matrix on
Clue Kids Jaws Babe Big1 4 3 1 5 22 4 5 1 5 2
Ground Truth
Clue Kids Jaws Babe Big
Y Y Y
My Recommendations
Ground TruthFeatures
to predict then compare predictions and truth