The document compares different algorithms and methods for predicting video viewing patterns and evaluating recommendation accuracy. It shows various graphs and metrics for techniques like evaluating daily and weekly viewing similarities, repeat search patterns, linear station-level viewing similarities, and recall vs resolution for different content recommendation systems. Overall network bandwidth savings from predictive caching strategies are also illustrated.

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0 168 336 504 672 840
Similarity
Δ Hours
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0 168 336 504 672 840
Similarity
Δ Hours
Daily pattern:
views in similar hours of the
days are more similar
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0 168 336 504 672 840
Similarity
Δ Hours
Weekly pattern:
views in similar days of the
week are more similar
Daily pattern:
views in similar hours of the
days are more similar
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0 168 336 504 672 840
Similarity
Δ Hours
Weekly pattern:
views in similar days of the
week are more similar
Daily pattern:
views in similar hours of the
days are more similar
Overall decay:
recent views are more similar
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0 168 336
Similarity
∆ Hours
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0 168 336 504 672 840
Similarity
Δ Hours
VOD Daypart View Similarity

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0 168 336 504 672 840
Similarity
VOD Daypart View Similarity
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0 168 336
Repeatpercentage
∆ Hours
Repeated-Search Pattern
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0 168 336
Similarity
∆ Hours
Linear(Station-level) View Similarity

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12. Number of Signals
0.7
7
= New Signal
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0%
20%
40%
60%
80%
100%
Top 5 Top 10 Top 20
Only Social
Nielsen + DVR
Nielsen + DVR +
Social

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18. 20.00
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65.00
70.00
0.40 0.60 0.80 1.00 1.20 1.40 1.60
Recall
Resolution (Weighted Sum, Normalized by User Study)
Recall vs. Resolution
BaseLine
CoMPASS
CoMPASS++
ViaccessOrca
DigitalSmith
Rovi
ContentWise
Gravity
MortarData
ThinkAnalytics
Compass (Trend)
Various
internal
and
external
Algorithms

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26. Picture courtesy of Cisco

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29. Baseline w/
100% cache
size
NetworkBWSavedbyCache

30. Baseline w/
100% cache
size
Prediction w/
30% cache
size
NetworkBWSavedbyCache

31. Baseline w/
100% cache
size
Prediction w/
30% cache
size
Prediction w/
60% cache size
NetworkBWSavedbyCache

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