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Personalized recommendation of linear content on interactive TV platforms
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Personalized recommendation of linear content on interactive TV platforms D. Zibriczky, B. Hidasi, Z. Petres, D. Tikk International Workshop on TV and multimedia personalization July 16th 2012, Monteal, Canada
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Transcript of Personalized recommendation of linear content on interactive TV platforms
Personalized recommendation of
linear content on interactive TV
platforms
D. Zibriczky, B. Hidasi, Z. Petres, D. Tikk
International Workshop on TV and multimedia personalization
July 16th 2012, Monteal, Canada
Table of contents
• Introduction
• Problems
• Solutions
• Results
• Conclusion
Enter date in master2 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Introduction / Consumption trends
3 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Introduction / Electronic Program Guide
4 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Problems / Goal
• SaskTel
• Finding relevant content with minimal efforts
• Time-shifting
• Up-selling
• Increasing ARPU
Enter date in master5 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Problems / Difficulties
• Implicit feedbacks only
• Huge but noisy data set
• Cold start problem
• Small recommendable set
• Multiple users per household
Enter date in master6 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Solutions / Content-based filtering
Enter date in master7 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
M The SimpsonsHow I Met
Your MotherFuturama …
Genre = Animation 1 0 1 …
Genre = Comedy 1 1 1 …
… … … … …
Director = Matt Groening 1 0 1 …
Director = Carter Bays 0 1 0 …
Actor = Dan Castellaneta 1 0 0 …
Actor = Billy West 0 0 1 …
… … … … …
Solutions / Content-based filtering
Enter date in master8 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
M The SimpsonsHow I Met
Your MotherFuturama …
Genre = Animation 1 0 1 …
Genre = Comedy 1 1 1 …
… … … … …
Director = Matt Groening 1 0 1 …
Director = Carter Bays 0 1 0 …
Actor = Dan Castellaneta 1 0 0 …
Actor = Billy West 0 0 1 …
… … … … …
User 1
0.53
0.81
…
0.18
0.00
0.18
0.00
…
Solutions / Content-based filtering
• Prediction: Cosine similarity of vectors
Enter date in master9 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
M The SimpsonsHow I Met
Your MotherFuturama …
Genre = Animation 1 0 1 …
Genre = Comedy 1 1 1 …
… … … … …
Director = Matt Groening 1 0 1 …
Director = Carter Bays 0 1 0 …
Actor = Dan Castellaneta 1 0 0 …
Actor = Billy West 0 0 1 …
… … … … …
User 1
0.53
0.81
…
0.18
0.00
0.18
0.00
…
Solutions / Content-based filtering
• Prediction: Cosine similarity of vectors
• Trick: Term frequency based weighting (TFIDF)
Enter date in master10 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
M The SimpsonsHow I Met
Your MotherFuturama …
Genre = Animation 0.21 0 0.23 …
Genre = Comedy 0.13 0.16 0.14 …
… … … … …
Director = Matt Groening 0.46 0 0.53 …
Director = Carter Bays 0 0.61 0 …
Actor = Dan Castellaneta 0.61 0 0 …
Actor = Billy West 0 0 0.76 …
… … … … …
User 1
0.16
0.12
…
0.17
0.00
0.14
0.00
…
Solutions / Collaborative Filtering
Enter date in master11 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
R The SimpsonsHow I Met
Your MotherFuturama …
User 1 1 …
User 2 1 1 …
User 3 1 …
… … … … …
Solutions / Collaborative Filtering
Enter date in master12 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
R The SimpsonsHow I Met
Your MotherFuturama …
User 1 1 …
User 2 1 1 …
User 3 1 …
… … … … …
Item factorsi11 i21 i31 …
i21 i22 i32 …
User factors
u11 u12
u21 u22
u31 u32
… …
Solutions / Collaborative Filtering
• Prediction: Dot product of latent factors
Enter date in master13 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
R The SimpsonsHow I Met
Your MotherFuturama …
User 1 1 …
User 2 1 1 u2*i3 …
User 3 1 …
… … … … …
Item factorsi11 i21 i31 …
i21 i22 i32 …
User factors
u11 u12
u21 u22
u31 u32
… …
Solutions / Collaborative Filtering
• Prediction: Dot product of latent factors
• Trick: Approximation using least squares solution
Enter date in master14 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
R The SimpsonsHow I Met
Your MotherFuturama …
User 1 1 …
User 2 1 1 u2*i3 …
User 3 1 …
… … … … …
Item factorsi11 i21 i31 …
i21 i22 i32 …
User factors
u11 u12
u21 u22
u31 u32
… …
Solutions / Combined filtering
Enter date in master15 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
R The SimpsonsHow I Met
Your MotherFuturama …
User 1 1 …
User 2 1 1 …
User 3 1 …
… … … … …
M The SimpsonsHow I Met
Your MotherFuturama …
Genre = Animation 1 0 1 …
Genre = Comedy 1 1 1 …
… … … … …
Director = Matt Groening 1 0 1 …
Director = Carter Bays 0 1 0 …
Actor = Dan Castellaneta 1 0 0 …
Actor = Billy West 0 0 1 …
… … … … …
Solutions / Combined filtering
Enter date in master16 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
R* The SimpsonsHow I Met
Your MotherFuturama …
User 1 1 0 0 …
User 2 1 1 0 …
User 3 0 1 0 …
… … … … …
Genre = Animation 1 0 1 …
Genre = Comedy 1 1 1 …
… … … … …
Director = Matt Groening 1 0 1 …
Director = Carter Bays 0 1 0 …
Actor = Dan Castellaneta 1 0 0 …
Actor = Billy West 0 0 1 …
… … … … …
Solutions / Combined filtering
Enter date in master17 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
R* The SimpsonsHow I Met
Your MotherFuturama …
User 1 1 0 0 …
User 2 1 1 0 …
User 3 0 1 0 …
… … … … …
Genre = Animation 1 0 1 …
Genre = Comedy 1 1 1 …
… … … … …
Director = Matt Groening 1 0 1 …
Director = Carter Bays 0 1 0 …
Actor = Dan Castellaneta 1 0 0 …
Actor = Billy West 0 0 1 …
… … … … …
User factors
u11 u12
u21 u22
u31 u32
… …
pu11 pu12
pu22 pu22
… …
… …
… …
… …
… …
… …
Item factorsi11 i21 I31 …
i21 i22 I32 …
Solutions / Channel recommendation
• Time period: 4:00-12:00
Enter date in master18 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
R (4:00-12:00)ChannelSports 1
ChannelSports 2
ChannelNews 1
…
User 1 1 1 …
User 2 1 …
User 3 1 …
… … … … …
Solutions / Channel recommendation
• Time period : 12:00-20:00
Enter date in master19 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
R (12:00-20:00)ChannelSports 1
ChannelSports 2
ChannelNews 1
…
User 1 1 …
User 2 1 1 …
User 3 1 …
… … … … …
Solutions / Channel recommendation
• Time period : 20:00-4:00
Enter date in master20 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
R (20:00-4:00)ChannelSports 1
ChannelSports 2
ChannelNews 1
…
User 1 1 …
User 2 1 …
User 3 …
… … … … …
Solutions / Channel recommendation
• Tensor factorization
Enter date in master21 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
R (4:00-12:00)ChannelSports 1
ChannelSports 2
ChannelNews 1
…
User 1 1 1 …
User 2 1 …
User 3 1 …
… … … … …
R (12:00-20:00)ChannelSports 1
ChannelSports 2
ChannelNews 1
…
User 1 1 …
User 2 1 1 …
User 3 1 …
… … … … …
R (20:00-4:00)ChannelSports 1
ChannelSports 2
ChannelNews 1
…
User 1 1 …
User 2 1 …
User 3 …
… … … … …
User factors
u11 u12
u21 u22
u31 u32
… …
Item factorsi11 i21 i31 …
i21 i22 i32 …
TP factorst11
t12t21
t22t31
t32
TP factors
Solutions / Channel recommendation
• Tensor factorization
• Prediction: Hadamard product of latent factors
Enter date in master22 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
R (4:00-12:00)ChannelSports 1
ChannelSports 2
ChannelNews 1
…
User 1 1 1 …
User 2 1 …
User 3 1 …
… … … … …
R (12:00-20:00)ChannelSports 1
ChannelSports 2
ChannelNews 1
…
User 1 1 …
User 2 1 1 …
User 3 1 …
… … … … …
R (20:00-4:00)ChannelSports 1
ChannelSports 2
ChannelNews 1
…
User 1 1 …
User 2 1 …
User 3 u3°i2°t3 …
… … … … …
User factors
u11 u12
u21 u22
u31 u32
… …
Item factorsi11 i21 i31 …
i21 i22 i32 …
t11
t12t21
t22t31
t32
TP factors
Solutions / Channel recommendation
• Tensor factorization
• Prediction: Hadamard product of latent factors
• Trick: Duration based modeling
Enter date in master23 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
R (4:00-12:00)ChannelSports 1
ChannelSports 2
ChannelNews 1
…
User 1 1 1 …
User 2 1 …
User 3 1 …
… … … … …
R (12:00-20:00)ChannelSports 1
ChannelSports 2
ChannelNews 1
…
User 1 1 …
User 2 1 1 …
User 3 1 …
… … … … …
R (20:00-4:00)ChannelSports 1
ChannelSports 2
ChannelNews 1
…
User 1 1 …
User 2 1 …
User 3 u3°i2°t3 …
… … … … …
User factors
u11 u12
u21 u22
u31 u32
… …
Item factorsi11 i21 i31 …
i21 i22 i32 …
t11
t12t21
t22t31
t32
Solutions / Baselines
• Most popular channels
• Most popular series
• Already seen contents
• Users’s favourite channels
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Solutions / Preprocessing
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Original set
308M
Solutions / Preprocessing
Enter date in master26 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Original set
308M
82M
filtering by event type
1
1
Solutions / Preprocessing
Enter date in master27 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Original set
308M
82M
23M
filtering by event type
1
2
1
2 filtering by leave-on and duration
Solutions / Preprocessing
Enter date in master28 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Original set
308M
82M
23M
Train set
22M
Test set
676K
filtering by event type
1
2
3 3
1
2
3
filtering by leave-on and duration
splitting by time
Solutions / Item grouping
Enter date in master29 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Results / Evaluation metrics
• Top N lists for all test users
• Recall @ N
• Coverage @ N
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Results / Evaluation metrics
• Top N lists for all test users
• Recall @ N
• Coverage @ N
• N = 9
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Results / Algorithms
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Algorithm Type Recall@9 Coverage@9
Most popular channels Pop 0.1151 0.1916
Most popular series Pop 0.1722 0.0497
Favourite channels (w duration) Pop 0.2773 0.9996
Already seen programs and series Pop 0.3911 0.8403
Cosine similarity CBF 0.4285 0.9596
Channel ITALS (w/o duration) CF 0.3140 0.9146
Channel ITALS (w duration) CF 0.3360 0.8170
IALS1 CF 0.3612 0.7867
Combined IALS1 HF 0.4054 0.7911
Blend (Linear combination) 0.4534 0.8941
Results / Algorithms
Enter date in master33 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Algorithm Type Recall@9 Coverage@9
Most popular channels Pop 0.1151 0.1916
Most popular series Pop 0.1722 0.0497
Favourite channels (w duration) Pop 0.2773 0.9996
Already seen programs and series Pop 0.3911 0.8403
Cosine similarity CBF 0.4285 0.9596
Channel ITALS (w/o duration) CF 0.3140 0.9146
Channel ITALS (w duration) CF 0.3360 0.8170
IALS1 CF 0.3612 0.7867
Combined IALS1 HF 0.4054 0.7911
Blend (Linear combination) 0.4534 0.8941
Results / Algorithms
Enter date in master34 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Algorithm Type Recall@9 Coverage@9
Most popular channels Pop 0.1151 0.1916
Most popular series Pop 0.1722 0.0497
Favourite channels (w duration) Pop 0.2773 0.9996
Already seen programs and series Pop 0.3911 0.8403
Cosine similarity CBF 0.4285 0.9596
Channel ITALS (w/o duration) CF 0.3140 0.9146
Channel ITALS (w duration) CF 0.3360 0.8170
IALS1 CF 0.3612 0.7867
Combined IALS1 HF 0.4054 0.7911
Blend (Linear combination) 0.4534 0.8941
Results / Algorithms
Enter date in master35 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Algorithm Type Recall@9 Coverage@9
Most popular channels Pop 0.1151 0.1916
Most popular series Pop 0.1722 0.0497
Favourite channels (w duration) Pop 0.2773 0.9996
Already seen programs and series Pop 0.3911 0.8403
Cosine similarity CBF 0.4285 0.9596
Channel ITALS (w/o duration) CF 0.3140 0.9146
Channel ITALS (w duration) CF 0.3360 0.8170
IALS1 CF 0.3612 0.7867
Combined IALS1 HF 0.4054 0.7911
Blend (Linear combination) 0.4534 0.8941
Results / Algorithms
Enter date in master36 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Algorithm Type Recall@9 Coverage@9
Most popular channels Pop 0.1151 0.1916
Most popular series Pop 0.1722 0.0497
Favourite channels (w duration) Pop 0.2773 0.9996
Already seen programs and series Pop 0.3911 0.8403
Cosine similarity CBF 0.4285 0.9596
Channel ITALS (w/o duration) CF 0.3140 0.9146
Channel ITALS (w duration) CF 0.3360 0.8170
IALS1 CF 0.3612 0.7867
Combined IALS1 HF 0.4054 0.7911
Blend (Linear combination) 0.4534 0.8941
Results / Evaluation on item partitions
Enter date in master37 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Algorithm old items new items popular long-tail series non-series
Already seen programs and series 0.3970 0.0000 0.4239 0.1221 0.4054 0.0104
Cosine similarity 0.4369 0.2670 0.4419 0.3560 0.4448 0.2220
Channel ITALS (w duration) 0.3441 0.1829 0.3620 0.1949 0.3555 0.0857
Combined IALS1 0.4266 0.0317 0.4495 0.1643 0.4358 0.0105
Blend (Linear combination) 0.4656 0.2154 0.4725 0.3485 0.4742 0.1871
Results / Evaluation on item partitions
Enter date in master38 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Algorithm old items new items popular long-tail series non-series
Already seen programs and series 0.3970 0.0000 0.4239 0.1221 0.4054 0.0104
Cosine similarity 0.4369 0.2670 0.4419 0.3560 0.4448 0.2220
Channel ITALS (w duration) 0.3441 0.1829 0.3620 0.1949 0.3555 0.0857
Combined IALS1 0.4266 0.0317 0.4495 0.1643 0.4358 0.0105
Blend (Linear combination) 0.4656 0.2154 0.4725 0.3485 0.4742 0.1871
Results / Evaluation on item partitions
Enter date in master39 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Algorithm old items new items popular long-tail series non-series
Already seen programs and series 0.3970 0.0000 0.4239 0.1221 0.4054 0.0104
Cosine similarity 0.4369 0.2670 0.4419 0.3560 0.4448 0.2220
Channel ITALS (w duration) 0.3441 0.1829 0.3620 0.1949 0.3555 0.0857
Combined IALS1 0.4266 0.0317 0.4495 0.1643 0.4358 0.0105
Blend (Linear combination) 0.4656 0.2154 0.4725 0.3485 0.4742 0.1871
Results / Evaluation on item partitions
Enter date in master40 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Algorithm old items new items popular long-tail series non-series
Already seen programs and series 0.3970 0.0000 0.4239 0.1221 0.4054 0.0104
Cosine similarity 0.4369 0.2670 0.4419 0.3560 0.4448 0.2220
Channel ITALS (w duration) 0.3441 0.1829 0.3620 0.1949 0.3555 0.0857
Combined IALS1 0.4266 0.0317 0.4495 0.1643 0.4358 0.0105
Blend (Linear combination) 0.4656 0.2154 0.4725 0.3485 0.4742 0.1871
Results / Evaluation on item partitions
Enter date in master41 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
Algorithm old items new items popular long-tail series non-series
Already seen programs and series 0.3970 0.0000 0.4239 0.1221 0.4054 0.0104
Cosine similarity 0.4369 0.2670 0.4419 0.3560 0.4448 0.2220
Channel ITALS (w duration) 0.3441 0.1829 0.3620 0.1949 0.3555 0.0857
Combined IALS1 0.4266 0.0317 0.4495 0.1643 0.4358 0.0105
Blend (Linear combination) 0.4656 0.2154 0.4725 0.3485 0.4742 0.1871
Conclusion
• Recommending EPG programs is important
• Difference between VOD and live content recommendation
• Data preprocessing
• Large amount of re-aired and regular programs
• Cold start solutions
• Best results with meta data based modeling
• Additional improvement by combining CF and CBF
Enter date in master42 International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
International Workshop on TV and multimedia personalization, 2012, Montreal, Canada
